{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "本代码主要参考官方baseline:\n",
    "https://tianchi.aliyun.com/competition/entrance/531858/forum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 基础工具\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_columns', None)\n",
    "import warnings\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.special import jn\n",
    "from IPython.display import display, clear_output\n",
    "import time\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline\n",
    "\n",
    "## 数据处理\n",
    "from sklearn import preprocessing\n",
    "\n",
    "## 数据降维处理的\n",
    "from sklearn.decomposition import PCA,FastICA,FactorAnalysis,SparsePCA\n",
    "\n",
    "## 模型预测的\n",
    "import lightgbm as lgb\n",
    "import xgboost as xgb\n",
    "\n",
    "## 参数搜索和评价的\n",
    "from sklearn.model_selection import GridSearchCV,cross_val_score,StratifiedKFold,train_test_split\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape: (250000, 40)\n",
      "TestA data shape: (50000, 39)\n"
     ]
    }
   ],
   "source": [
    "path = './input/'\n",
    "## 1) 载入训练集和测试集；\n",
    "Train_data = pd.read_csv(path+'car_train_0110.csv', sep=' ')\n",
    "TestA_data = pd.read_csv(path+'car_testA_0110.csv', sep=' ')\n",
    "\n",
    "## 输出数据的大小信息\n",
    "print('Train data shape:',Train_data.shape)\n",
    "print('TestA data shape:',TestA_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1e5141b48>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Train_data.price.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#预处理函数\n",
    "def reduce_mem(df): \n",
    "    \"\"\" iterate through all the columns of a dataframe and modify the data type  to reduce memory usage.             \"\"\" \n",
    "    start_mem = df.memory_usage().sum() / 1024**2 \n",
    "    print('Memory usage of dataframe is {:.2f} MB'.format(start_mem)) \n",
    "    df1 = df.copy() \n",
    "    for col in df.columns: \n",
    "        col_type = df[col].dtype \n",
    "         \n",
    "        if col_type != object: \n",
    "            c_min = df[col].min() \n",
    "            c_max = df[col].max() \n",
    "            if str(col_type)[:3] == 'int': \n",
    "                if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max: \n",
    "                    df[col] = df[col].astype(np.int8) \n",
    "                elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max: \n",
    "                    df[col] = df[col].astype(np.int16) \n",
    "                elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max: \n",
    "                    df[col] = df[col].astype(np.int32) \n",
    "                elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max: \n",
    "                    df[col] = df[col].astype(np.int64)   \n",
    "            else: \n",
    "                if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max: \n",
    "                    df[col] = df[col].astype(np.float16) \n",
    "                elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max: \n",
    "                    df[col] = df[col].astype(np.float32) \n",
    "                else: \n",
    "                    df[col] = df[col].astype(np.float64) \n",
    "        #Treatment for category columns\n",
    "        else: \n",
    "            df[col] = df[col].astype('category') \n",
    " \n",
    "    end_mem = df.memory_usage().sum() / 1024**2 \n",
    "    print('Memory usage after optimization is: {:.2f} MB'.format(end_mem)) \n",
    "    print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem)) \n",
    "    if end_mem > start_mem:\n",
    "        print(f'Memory usage increases! Return the original df.')\n",
    "        return df1\n",
    "    return df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Memory usage of dataframe is 76.29 MB\n",
      "Memory usage after optimization is: 20.74 MB\n",
      "Decreased by 72.8%\n",
      "Memory usage of dataframe is 14.88 MB\n",
      "Memory usage after optimization is: 3.96 MB\n",
      "Decreased by 73.4%\n"
     ]
    }
   ],
   "source": [
    "Train_data = reduce_mem(Train_data)\n",
    "TestA_data = reduce_mem(TestA_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-0.947266</td>\n",
       "      <td>9.078125</td>\n",
       "      <td>0.581055</td>\n",
       "      <td>3.945312</td>\n",
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       "      <td>306648</td>\n",
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       "      <td>9</td>\n",
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       "      <td>173</td>\n",
       "      <td>15.0</td>\n",
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       "      <td>-3.021484</td>\n",
       "      <td>-0.419434</td>\n",
       "      <td>-1.417969</td>\n",
       "      <td>-3.736328</td>\n",
       "      <td>4.558594</td>\n",
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       "      <td>0.086365</td>\n",
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       "      <td>0.055450</td>\n",
       "      <td>0.122986</td>\n",
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       "      <td>-0.658203</td>\n",
       "      <td>-3.949219</td>\n",
       "      <td>4.593750</td>\n",
       "      <td>-1.145508</td>\n",
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       "      <td>340675</td>\n",
       "      <td>25347</td>\n",
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       "      <td>18.0</td>\n",
       "      <td>12</td>\n",
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       "      <td>54</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.087646</td>\n",
       "      <td>0.138062</td>\n",
       "      <td>0.055634</td>\n",
       "      <td>0.122925</td>\n",
       "      <td>0.000099</td>\n",
       "      <td>0.001656</td>\n",
       "      <td>-4.476562</td>\n",
       "      <td>0.124146</td>\n",
       "      <td>1.364258</td>\n",
       "      <td>-0.319824</td>\n",
       "      <td>-1.131836</td>\n",
       "      <td>-3.302734</td>\n",
       "      <td>-1.998047</td>\n",
       "      <td>-1.279297</td>\n",
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      ],
      "text/plain": [
       "   SaleID    name   regDate  model  brand  bodyType  fuelType  gearbox  power  \\\n",
       "0  134890     734  20160002   13.0      9       NaN       0.0      1.0      0   \n",
       "1  306648  196973  20080307   72.0      9       7.0       5.0      1.0    173   \n",
       "2  340675   25347  20020312   18.0     12       3.0       0.0      1.0     50   \n",
       "3   57332    5382  20000611   38.0      8       7.0       0.0      1.0     54   \n",
       "4  265235  173174  20030109   87.0      0       5.0       5.0      1.0    131   \n",
       "\n",
       "   kilometer  notRepairedDamage  regionCode  seller  offerType  creatDate  \\\n",
       "0       15.0                NaN         725       1          0   20160316   \n",
       "1       15.0                1.0        2568       1          0   20160311   \n",
       "2       12.5                1.0         724       1          0   20160323   \n",
       "3       15.0                1.0         498       1          0   20160330   \n",
       "4        3.0                1.0        1273       1          0   20160307   \n",
       "\n",
       "   price      v_0        v_1       v_2       v_3        v_4       v_5  \\\n",
       "0    520  60.5625  16.671875  1.416992  1.652344 -11.867188 -0.456787   \n",
       "1   5500  73.0000  -4.179688 -0.656250 -3.021484  -0.419434 -1.417969   \n",
       "2   1100  69.8125  -4.199219  0.025879  2.769531  -0.645020  1.100586   \n",
       "3   1200  69.8125  -4.378906 -0.595703  3.841797   0.119080  1.519531   \n",
       "4   3300  73.0625  -3.179688 -0.447021  1.324219  -0.233521 -1.135742   \n",
       "\n",
       "        v_6       v_7       v_8       v_9      v_10      v_11      v_12  \\\n",
       "0  9.085938  0.566895  0.000000  1.124023  0.000000  0.054291  0.053497   \n",
       "1 -3.736328  4.558594  0.314453  0.081787  0.086365  0.134644  0.055450   \n",
       "2  2.593750 -0.873535  0.315918  0.073792  0.081787  0.085144  0.053528   \n",
       "3  2.955078 -0.814941  0.315430  0.078308  0.086731  0.076416  0.053528   \n",
       "4 -3.294922 -1.981445  0.315430  0.121277  0.087646  0.138062  0.055634   \n",
       "\n",
       "       v_13      v_14      v_15       v_16      v_17      v_18       v_19  \\\n",
       "0  0.124329  0.092163  0.000000  18.765625 -1.511719 -1.008789 -12.101562   \n",
       "1  0.122986  0.001070  0.122314  -5.687500 -0.489990 -2.224609  -0.226807   \n",
       "2  0.123413  0.064392  0.003345  -3.294922  1.816406  3.554688  -0.683594   \n",
       "3  0.123474  0.069214  0.000000  -3.406250  1.498047  4.781250   0.039093   \n",
       "4  0.122925  0.000099  0.001656  -4.476562  0.124146  1.364258  -0.319824   \n",
       "\n",
       "       v_20      v_21      v_22      v_23  \n",
       "0 -0.947266  9.078125  0.581055  3.945312  \n",
       "1 -0.658203 -3.949219  4.593750 -1.145508  \n",
       "2  0.971680  2.625000 -0.852051 -1.246094  \n",
       "3  1.227539  3.041016 -0.801758 -1.251953  \n",
       "4 -1.131836 -3.302734 -1.998047 -1.279297  "
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       "      <td>20160402</td>\n",
       "      <td>73.5625</td>\n",
       "      <td>-4.378906</td>\n",
       "      <td>-0.051392</td>\n",
       "      <td>-2.531250</td>\n",
       "      <td>0.145752</td>\n",
       "      <td>2.140625</td>\n",
       "      <td>3.324219</td>\n",
       "      <td>4.031250</td>\n",
       "      <td>0.314697</td>\n",
       "      <td>0.100708</td>\n",
       "      <td>0.086426</td>\n",
       "      <td>0.089417</td>\n",
       "      <td>0.053314</td>\n",
       "      <td>0.122986</td>\n",
       "      <td>0.083313</td>\n",
       "      <td>0.105408</td>\n",
       "      <td>-6.000000</td>\n",
       "      <td>0.147095</td>\n",
       "      <td>-1.903320</td>\n",
       "      <td>0.348877</td>\n",
       "      <td>2.324219</td>\n",
       "      <td>3.343750</td>\n",
       "      <td>4.046875</td>\n",
       "      <td>-1.431641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>714316</td>\n",
       "      <td>1836</td>\n",
       "      <td>20010301</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>75</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1312</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>20160323</td>\n",
       "      <td>70.1875</td>\n",
       "      <td>-3.949219</td>\n",
       "      <td>0.551758</td>\n",
       "      <td>2.400391</td>\n",
       "      <td>-0.082825</td>\n",
       "      <td>1.447266</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>-1.193359</td>\n",
       "      <td>0.315430</td>\n",
       "      <td>0.084351</td>\n",
       "      <td>0.081909</td>\n",
       "      <td>0.083374</td>\n",
       "      <td>0.051422</td>\n",
       "      <td>0.123413</td>\n",
       "      <td>0.074463</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.287109</td>\n",
       "      <td>2.082031</td>\n",
       "      <td>2.937500</td>\n",
       "      <td>-0.123047</td>\n",
       "      <td>1.202148</td>\n",
       "      <td>3.570312</td>\n",
       "      <td>-1.180664</td>\n",
       "      <td>-1.348633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>704693</td>\n",
       "      <td>212291</td>\n",
       "      <td>20170610</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4187</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>20160322</td>\n",
       "      <td>68.0000</td>\n",
       "      <td>3.242188</td>\n",
       "      <td>9.953125</td>\n",
       "      <td>-0.249512</td>\n",
       "      <td>-13.078125</td>\n",
       "      <td>-4.574219</td>\n",
       "      <td>-0.487793</td>\n",
       "      <td>-1.722656</td>\n",
       "      <td>0.289551</td>\n",
       "      <td>0.217896</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.138916</td>\n",
       "      <td>0.055603</td>\n",
       "      <td>0.130737</td>\n",
       "      <td>0.002031</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.367188</td>\n",
       "      <td>8.250000</td>\n",
       "      <td>-4.136719</td>\n",
       "      <td>-13.335938</td>\n",
       "      <td>-4.445312</td>\n",
       "      <td>-0.707031</td>\n",
       "      <td>-1.720703</td>\n",
       "      <td>3.568359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>624972</td>\n",
       "      <td>1345</td>\n",
       "      <td>19820005</td>\n",
       "      <td>215.0</td>\n",
       "      <td>32</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7023</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>20160309</td>\n",
       "      <td>69.0000</td>\n",
       "      <td>-3.689453</td>\n",
       "      <td>-1.526367</td>\n",
       "      <td>2.986328</td>\n",
       "      <td>0.334229</td>\n",
       "      <td>2.916016</td>\n",
       "      <td>5.226562</td>\n",
       "      <td>6.058594</td>\n",
       "      <td>0.288086</td>\n",
       "      <td>0.150879</td>\n",
       "      <td>0.086670</td>\n",
       "      <td>0.036407</td>\n",
       "      <td>0.053314</td>\n",
       "      <td>0.123535</td>\n",
       "      <td>0.098816</td>\n",
       "      <td>0.100891</td>\n",
       "      <td>-2.537109</td>\n",
       "      <td>0.514160</td>\n",
       "      <td>4.414062</td>\n",
       "      <td>0.357666</td>\n",
       "      <td>2.701172</td>\n",
       "      <td>5.324219</td>\n",
       "      <td>6.085938</td>\n",
       "      <td>-0.900391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>669753</td>\n",
       "      <td>1428</td>\n",
       "      <td>20060205</td>\n",
       "      <td>30.0</td>\n",
       "      <td>4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>122</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5977</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>20160308</td>\n",
       "      <td>74.3750</td>\n",
       "      <td>-4.113281</td>\n",
       "      <td>-0.301025</td>\n",
       "      <td>-1.512695</td>\n",
       "      <td>-0.444824</td>\n",
       "      <td>2.349609</td>\n",
       "      <td>4.082031</td>\n",
       "      <td>-2.570312</td>\n",
       "      <td>0.315430</td>\n",
       "      <td>0.117249</td>\n",
       "      <td>0.086792</td>\n",
       "      <td>0.108948</td>\n",
       "      <td>0.055664</td>\n",
       "      <td>0.123108</td>\n",
       "      <td>0.088379</td>\n",
       "      <td>0.002508</td>\n",
       "      <td>-6.199219</td>\n",
       "      <td>-0.191772</td>\n",
       "      <td>-1.224609</td>\n",
       "      <td>-0.326904</td>\n",
       "      <td>2.255859</td>\n",
       "      <td>4.183594</td>\n",
       "      <td>-2.574219</td>\n",
       "      <td>0.014206</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SaleID    name   regDate  model  brand  bodyType  fuelType  gearbox  power  \\\n",
       "0  720326     505  20060505   19.0     13       7.0       0.0      1.0     90   \n",
       "1  714316    1836  20010301    5.0      5       3.0       4.0      1.0     75   \n",
       "2  704693  212291  20170610    6.0     18       NaN       5.0      0.0    150   \n",
       "3  624972    1345  19820005  215.0     32       7.0       0.0      1.0      0   \n",
       "4  669753    1428  20060205   30.0      4       7.0       5.0      1.0    122   \n",
       "\n",
       "   kilometer  notRepairedDamage  regionCode  seller  offerType  creatDate  \\\n",
       "0        8.0                1.0         479       1          0   20160402   \n",
       "1       15.0                1.0        1312       1          0   20160323   \n",
       "2       15.0                0.0        4187       1          0   20160322   \n",
       "3        6.0                0.0        7023       1          0   20160309   \n",
       "4       15.0                1.0        5977       1          0   20160308   \n",
       "\n",
       "       v_0       v_1       v_2       v_3        v_4       v_5       v_6  \\\n",
       "0  73.5625 -4.378906 -0.051392 -2.531250   0.145752  2.140625  3.324219   \n",
       "1  70.1875 -3.949219  0.551758  2.400391  -0.082825  1.447266  3.500000   \n",
       "2  68.0000  3.242188  9.953125 -0.249512 -13.078125 -4.574219 -0.487793   \n",
       "3  69.0000 -3.689453 -1.526367  2.986328   0.334229  2.916016  5.226562   \n",
       "4  74.3750 -4.113281 -0.301025 -1.512695  -0.444824  2.349609  4.082031   \n",
       "\n",
       "        v_7       v_8       v_9      v_10      v_11      v_12      v_13  \\\n",
       "0  4.031250  0.314697  0.100708  0.086426  0.089417  0.053314  0.122986   \n",
       "1 -1.193359  0.315430  0.084351  0.081909  0.083374  0.051422  0.123413   \n",
       "2 -1.722656  0.289551  0.217896  0.000000  0.138916  0.055603  0.130737   \n",
       "3  6.058594  0.288086  0.150879  0.086670  0.036407  0.053314  0.123535   \n",
       "4 -2.570312  0.315430  0.117249  0.086792  0.108948  0.055664  0.123108   \n",
       "\n",
       "       v_14      v_15      v_16      v_17      v_18       v_19      v_20  \\\n",
       "0  0.083313  0.105408 -6.000000  0.147095 -1.903320   0.348877  2.324219   \n",
       "1  0.074463  0.000000 -3.287109  2.082031  2.937500  -0.123047  1.202148   \n",
       "2  0.002031  0.000000  4.367188  8.250000 -4.136719 -13.335938 -4.445312   \n",
       "3  0.098816  0.100891 -2.537109  0.514160  4.414062   0.357666  2.701172   \n",
       "4  0.088379  0.002508 -6.199219 -0.191772 -1.224609  -0.326904  2.255859   \n",
       "\n",
       "       v_21      v_22      v_23  \n",
       "0  3.343750  4.046875 -1.431641  \n",
       "1  3.570312 -1.180664 -1.348633  \n",
       "2 -0.707031 -1.720703  3.568359  \n",
       "3  5.324219  6.085938 -0.900391  \n",
       "4  4.183594 -2.574219  0.014206  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 通过.head() 简要浏览读取数据的形式\n",
    "display(Train_data.head())\n",
    "display(TestA_data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['SaleID', 'name', 'regDate', 'model', 'brand', 'bodyType', 'fuelType',\n",
      "       'gearbox', 'power', 'kilometer', 'notRepairedDamage', 'regionCode',\n",
      "       'seller', 'offerType', 'creatDate', 'price', 'v_0', 'v_1', 'v_2', 'v_3',\n",
      "       'v_4', 'v_5', 'v_6', 'v_7', 'v_8', 'v_9', 'v_10', 'v_11', 'v_12',\n",
      "       'v_13', 'v_14', 'v_15', 'v_16', 'v_17', 'v_18', 'v_19', 'v_20', 'v_21',\n",
      "       'v_22', 'v_23'],\n",
      "      dtype='object')\n",
      "Index([], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#提取数值类型特征列名\n",
    "numerical_cols = Train_data.select_dtypes(exclude = 'object').columns\n",
    "print(numerical_cols)\n",
    "categorical_cols = Train_data.select_dtypes(include = 'object').columns\n",
    "print(categorical_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "Train_data['notRepairedDamage'].fillna(-1,inplace=True)\n",
    "TestA_data['notRepairedDamage'].fillna(-1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "date_features = ['regDate', 'creatDate']\n",
    "numeric_features = ['power', 'kilometer'] + ['v_{}'.format(i) for i in range(15)]\n",
    "categorical_features = ['name', 'model', 'brand', 'bodyType', 'fuelType', 'gearbox', 'notRepairedDamage', 'regionCode',]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00,  2.65it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 12.76it/s]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "#时间特征处理\n",
    "def date_proc(x):\n",
    "    m = int(x[4:6])\n",
    "    if m == 0:\n",
    "        m = 1\n",
    "    return x[:4] + '-' + str(m) + '-' + x[6:]\n",
    "\n",
    "def num_to_date(df,date_cols):\n",
    "    for f in tqdm(date_cols):\n",
    "        df[f] = pd.to_datetime(df[f].astype('str').apply(date_proc))\n",
    "        df[f + '_year'] = df[f].dt.year\n",
    "        df[f + '_month'] = df[f].dt.month\n",
    "        df[f + '_day'] = df[f].dt.day\n",
    "        df[f + '_dayofweek'] = df[f].dt.dayofweek\n",
    "    return df\n",
    "Train_data = num_to_date(Train_data,date_features)\n",
    "TestA_data = num_to_date(TestA_data,date_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "时间特征相似，所以可以采用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import mode\n",
    "#类别统计\n",
    "def sta_cate(df,cols):\n",
    "    sta_df = pd.DataFrame(columns = ['column','nunique','miss_rate','most_value','most_value_counts','max_value_counts_rate'])\n",
    "    for col in cols:\n",
    "        count = df[col].count()\n",
    "        nunique = df[col].nunique()\n",
    "        miss_rate = (df.shape[0] - count) / df.shape[0]\n",
    "        most_value = df[col].value_counts().index[0]\n",
    "        most_value_counts = df[col].value_counts().values[0]\n",
    "        max_value_counts_rate = most_value_counts / df.shape[0]\n",
    "        \n",
    "        sta_df = sta_df.append({'column':col,'nunique':nunique,'miss_rate':miss_rate,'most_value':most_value,\n",
    "                                'most_value_counts':most_value_counts,'max_value_counts_rate':max_value_counts_rate},ignore_index=True)\n",
    "    return sta_df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>column</th>\n",
       "      <th>nunique</th>\n",
       "      <th>miss_rate</th>\n",
       "      <th>most_value</th>\n",
       "      <th>most_value_counts</th>\n",
       "      <th>max_value_counts_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>name</td>\n",
       "      <td>164312</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>451</td>\n",
       "      <td>452</td>\n",
       "      <td>0.001808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>model</td>\n",
       "      <td>251</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>20344</td>\n",
       "      <td>0.081376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>brand</td>\n",
       "      <td>40</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>53699</td>\n",
       "      <td>0.214796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>bodyType</td>\n",
       "      <td>8</td>\n",
       "      <td>0.101520</td>\n",
       "      <td>7</td>\n",
       "      <td>64571</td>\n",
       "      <td>0.258284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>fuelType</td>\n",
       "      <td>7</td>\n",
       "      <td>0.089960</td>\n",
       "      <td>0</td>\n",
       "      <td>150664</td>\n",
       "      <td>0.602656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>gearbox</td>\n",
       "      <td>2</td>\n",
       "      <td>0.054052</td>\n",
       "      <td>1</td>\n",
       "      <td>184645</td>\n",
       "      <td>0.738580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>notRepairedDamage</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>176922</td>\n",
       "      <td>0.707688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>regionCode</td>\n",
       "      <td>8081</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>487</td>\n",
       "      <td>550</td>\n",
       "      <td>0.002200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              column nunique  miss_rate most_value most_value_counts  \\\n",
       "0               name  164312   0.000000        451               452   \n",
       "1              model     251   0.000000          0             20344   \n",
       "2              brand      40   0.000000          0             53699   \n",
       "3           bodyType       8   0.101520          7             64571   \n",
       "4           fuelType       7   0.089960          0            150664   \n",
       "5            gearbox       2   0.054052          1            184645   \n",
       "6  notRepairedDamage       3   0.000000          1            176922   \n",
       "7         regionCode    8081   0.000000        487               550   \n",
       "\n",
       "   max_value_counts_rate  \n",
       "0               0.001808  \n",
       "1               0.081376  \n",
       "2               0.214796  \n",
       "3               0.258284  \n",
       "4               0.602656  \n",
       "5               0.738580  \n",
       "6               0.707688  \n",
       "7               0.002200  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sta_cate(Train_data,categorical_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>column</th>\n",
       "      <th>nunique</th>\n",
       "      <th>miss_rate</th>\n",
       "      <th>most_value</th>\n",
       "      <th>most_value_counts</th>\n",
       "      <th>max_value_counts_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>name</td>\n",
       "      <td>38668</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>73</td>\n",
       "      <td>98</td>\n",
       "      <td>0.00196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>model</td>\n",
       "      <td>249</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>3916</td>\n",
       "      <td>0.07832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>brand</td>\n",
       "      <td>40</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>10697</td>\n",
       "      <td>0.21394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>bodyType</td>\n",
       "      <td>8</td>\n",
       "      <td>0.10220</td>\n",
       "      <td>7</td>\n",
       "      <td>12748</td>\n",
       "      <td>0.25496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>fuelType</td>\n",
       "      <td>7</td>\n",
       "      <td>0.08804</td>\n",
       "      <td>0</td>\n",
       "      <td>30045</td>\n",
       "      <td>0.60090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>gearbox</td>\n",
       "      <td>2</td>\n",
       "      <td>0.05426</td>\n",
       "      <td>1</td>\n",
       "      <td>36935</td>\n",
       "      <td>0.73870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>notRepairedDamage</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>35555</td>\n",
       "      <td>0.71110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>regionCode</td>\n",
       "      <td>7078</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>487</td>\n",
       "      <td>122</td>\n",
       "      <td>0.00244</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              column nunique  miss_rate most_value most_value_counts  \\\n",
       "0               name   38668    0.00000         73                98   \n",
       "1              model     249    0.00000          0              3916   \n",
       "2              brand      40    0.00000          0             10697   \n",
       "3           bodyType       8    0.10220          7             12748   \n",
       "4           fuelType       7    0.08804          0             30045   \n",
       "5            gearbox       2    0.05426          1             36935   \n",
       "6  notRepairedDamage       3    0.00000          1             35555   \n",
       "7         regionCode    7078    0.00000        487               122   \n",
       "\n",
       "   max_value_counts_rate  \n",
       "0                0.00196  \n",
       "1                0.07832  \n",
       "2                0.21394  \n",
       "3                0.25496  \n",
       "4                0.60090  \n",
       "5                0.73870  \n",
       "6                0.71110  \n",
       "7                0.00244  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sta_cate(TestA_data,categorical_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bodyType:\n",
      "{0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, -1.0}\n",
      "{0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, -1.0}\n",
      "fuelType:\n",
      "{0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, -1.0}\n",
      "{0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, -1.0}\n",
      "gearbox:\n",
      "{0.0, 1.0, -1.0}\n",
      "{0.0, 1.0, -1.0}\n",
      "notRepairedDamage:\n",
      "{0.0, 1.0, -1.0}\n",
      "{0.0, 1.0, -1.0}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "def cate_encoder(df,df_test,cols):\n",
    "    le = LabelEncoder()\n",
    "    ohe = OneHotEncoder(sparse=False,categories ='auto')\n",
    "    \n",
    "    for col in cols:\n",
    "        print(col+':')\n",
    "        print(set(df[col]))\n",
    "        print(set(df_test[col]))\n",
    "        \n",
    "        le = le.fit(df[col])\n",
    "        integer_encoded = le.transform(df[col])\n",
    "        integer_encoded_test = le.transform(df_test[col])\n",
    "\n",
    "        # binary encode\n",
    "        integer_encoded = integer_encoded.reshape(len(integer_encoded), 1)\n",
    "        integer_encoded_test = integer_encoded_test.reshape(len(integer_encoded_test), 1)\n",
    "        ohe = ohe.fit(integer_encoded)\n",
    "        \n",
    "        onehot_encoded = ohe.transform(integer_encoded)\n",
    "        onehot_encoded_df = pd.DataFrame(onehot_encoded)\n",
    "        onehot_encoded_df.columns = list(map(lambda x:str(x)+'_'+col,onehot_encoded_df.columns.values))\n",
    "\n",
    "        onehot_encoded_test = ohe.transform(integer_encoded_test)\n",
    "        onehot_encoded_test_df = pd.DataFrame(onehot_encoded_test)\n",
    "        onehot_encoded_test_df.columns = list(map(lambda x:str(x)+'_'+col,onehot_encoded_test_df.columns.values))\n",
    "        \n",
    "        df =  pd.concat([df,onehot_encoded_df], axis=1)\n",
    "        df_test =  pd.concat([df_test,onehot_encoded_test_df], axis=1)\n",
    "    \n",
    "    return df,df_test\n",
    "cate_cols = ['bodyType', 'fuelType', 'gearbox', 'notRepairedDamage']\n",
    "Train_data[cate_cols] = Train_data[cate_cols].fillna(-1)\n",
    "TestA_data[cate_cols] = TestA_data[cate_cols].fillna(-1)\n",
    "Train_data,TestA_data = cate_encoder(Train_data,TestA_data,cate_cols)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 17 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 15))\n",
    "i = 1\n",
    "for col in numeric_features:\n",
    "    plt.subplot(5, 4, i)\n",
    "    i += 1\n",
    "    sns.distplot(Train_data[col], label='train', color='r', hist=False)\n",
    "    sns.distplot(TestA_data[col], label='test', color='b', hist=False)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "tr = Train_data.copy()\n",
    "te = TestA_data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         15.0\n",
       "1         15.0\n",
       "2         12.5\n",
       "3         15.0\n",
       "4          3.0\n",
       "          ... \n",
       "249995    15.0\n",
       "249996    15.0\n",
       "249997     3.0\n",
       "249998     9.0\n",
       "249999    12.5\n",
       "Name: kilometer, Length: 250000, dtype: float16"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train_data.kilometer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uAVi1alXeTRmjc0RheOLfWoAm3npxpmUbITP40z/l/A+v4Yt8iGeegTe+MSshSh5JTm7U1gbLdrY1sDjCmKTw5fP7jZIbGVByQybjYx+D556b2mOuXAlf/vL46++88042btzIc889x0MPPcQDDzzA008/jbtzxRVX8Itf/IJ9+/Zx/PHH85Of/ASAjo4OGhoa+OIXv8hjjz3GrFmzpjboSXD3a9Ks/yrw1XHWrQXWTmU8ek2l6OR35cZIhRbwKkGF1rUZ7vsg8LdJTUQvAW6dbEA6R8jRWveLHkoY4sx3Lzzyna++mvM/fid0wa9/reRGMWg7WAEEyY2ammDZzq6mCCOSXNP7zeE0LCUDGpYi09lDDz3EQw89xBve8AbOPPNMXnrpJbZu3crpp5/OI488wqc+9Sl++ctf0tDQEHWokiG9ppJzPT3BleHE7Tzi7oNAokLrReB+d99kZreb2RUAZnZ2WPX1XuCfzGxTuG8r8FmCBMk64PZw2bSmc8T0tW5zDcvKXia2eM6R7xyLccyHLudEmvn1z/WhtRi0HQyGoMyYAfPmQYkNszOupuiSO/n2fqPKjQxothSZjIkyn7ng7tx666185CMfGbPumWeeYe3atdx6661ccskl3HbbbRFEOP3oNZWi09MTfHpubc275AakrtBy99uSbq8jGHKSat97gXunMh6dI+RouMP6383n9+c8BZx6dAf5vd/jTV/5JT/+5QLcD+UkpQD199M+GAOgsREqKmB2TZzX4k3oxS8eer85nCo3MqBhKTLd1NXV0dXVBcCll17KvffeS3d3NwCvvvoqe/fuZc+ePdTU1PCBD3yAT37ykzz77LNj9pX8oddUInXwIMycGdzOw+SG6BxRCHZtG2T/4AzOXjaJZntvehPnlz7J/s4KtmyZutgkD3V10cYMykuHRoakNFQP0EE9xOPRxiYFLZ/fb1S5kQENS5HppqmpifPPP5/TTjuNyy+/nGuvvZbzzjsPgNraWv71X/+V5uZm/uIv/oKSkhLKy8v5+te/DsANN9zA5ZdfznHHHafGcnlEr6lEqqcHmpqguVnJjTylc8T0t+6/XgPmc/abjrCZaLKaGs5d2QfPwDPPwCmnTFl4km/C5MaMWD9m1QA0xAbp3F8PnZ2HOoyKTLF8fr9RciMDqtyQ6ej73//+Yff/7M/+7LD7J5xwApdeeumY/T760Y/y0Y9+NKuxydHRayqROXgQFof995XcyFs6R0xv6x/rooI+Tn/ngkkd56TLT6D8mX42rhuC91dPUXSSdzo7g+RG7QAQvM71tcN00BAkN44/Ptr4pKDl6/tNRsNSzOwyM9tiZs1mdkuK9ZVm9sNw/VNmtihp3a3h8i1mdmm6Y5rZTeEyN7NZScsvMLMOM3su/Lkt3bGmipIbIiJS1Hp6Dg1L6e2NNhaRArV+QwUr7AUqV5w8qeNUXHohJ7OFjb9un6LIJC8lKjcahkcWNdR7kNzQMDMpUmmTG2ZWCnwNuBxYBlxjZstGbXY90ObuJwJfAj4X7ruMYEq25cBlwN1mVprmmL8G3g7sShHOL919Zfhz+xHENymJpIaGpYiISFE6eDAYlgKq3BDJAnd4fs9s3jBzV9AZcjJWr+a00hfZ+PIkjyP5LVG50egjixoajU7CYSkiRSiTyo3VQLO7b3f3fuA+4MpR21wJfDe8/QBwkZlZuPw+d+9z9x1Ac3i8cY/p7r91951H8BwyiW9SVLkhR8Pd029UAIrleUJxPNdieI5yhNyDhEZ9PZSWKrkxgWL4+ymG5xiF1/Y4BwYaWHHiFPx9VVRw2vGt7Oxs0gX8Qpao3Jh5aFaU+sYSVW4UiWI4Fx/Nc8wkuTEXeCXpfku4LOU24XzzHUDTBPtmcsxUzjOz583sp2a2/AjimxQlN+RIVVVVceDAgYI/8bg7Bw4coKqqKupQsq4YXtNiej3lCAwMBG+E1dXBj5IbKekcIZOx4fFWAE5fPTU9Mk47LfjCu3njcJotZdpKVG7MKh1Z1NBURhf1DLercqOQ6f1mfJk0FE01SfLof8nxthlveaqkSrpX51lgobt3m9k7gP8LLM0wviBIsxuAGwAWLMi8WZNmS5EjNW/ePFpaWti3b1/UoWRdVVUV8+bNizqMrCuW17RYXk85AgfDaSlrapTcmIDOETIZL/z3XqCJ0y+dmiaQp7+5EX4KGx/fxznnHTMlx5T8MtzRRTuNzJg9MLKsflYwFKlrXy8NUQUmWaf3m/FlktxoAeYn3Z8H7BlnmxYzKwMagNY0+6Y75mHcvTPp9lozuztsOJpJfIn97gHuAVi1alXGqS5VbsiRKi8vZ3FiZgEpCHpNpWglkhmq3JiQzhEyGRueHWQerzDzzcvTb5yBRRcvpeZ/xdn4qw5AyY1C1LW/j2FKaZxz6Dpvw5xgGuGO/QNKbhQwvd+ML5NhKeuApWa22MwqCBqErhm1zRrguvD2VcCjHtTJrAGuDmdTWUxQafF0hsc8jJkdG/bxwMxWh7EfOJpjHSklN0REpGglV25UVSm5IZIFG3bWsaKmOehtMwVKTl/OcjazcXOqAmcpBG37gy8mM5oOfZ1raAquW3ceGEi5j0ihS5vcCHto3AQ8CLwI3O/um8zsdjO7Itzs20CTmTUDnwBuCffdBNwPbAZ+Btzo7kPjHRPAzG42sxaCCowNZvat8DGuAjaa2fPAXcDVHhj3WFNFs6WIiEjRUuWGSFYNDMCLHcdz+vyOqTtoZSXLZrzGi681Tt0xJa+0t4bJjRmHltU3BMmsjlZdkZXilMmwFNx9LbB21LLbkm73Au8dZ987gDsyOWa4/C6C5MXo5V8FvpppfFNJlRsiIlK0EpUbSm6IZMWW9V0MUMeKFVN73BMXDfLd386m56BTXaMKjkLT1ha8psnJjYZwLEpHR+E2mhSZSCbDUoqekhsiIlK0EsmMREPR3t5o4xEpMBvWtgCw4oKZU3rcE1fUALD9qcJuOlis2jqCr3GHVW6Eo5o6p7AISGQ6UXIjA5otRSRaZnavme01s43jrH+/mW0If35jZmckrdtpZi+Y2XNmtj53UYsUCA1LEcmqDU/EKaefk39v6ZQe98Szg2+9zb96fUqPK/mhrTOYAjZl5UaXvuJJcdL//AyockMkct8BLptg/Q7gre6+Avgs4axISS5095XuvipL8YkULk0FK5JVG16q4NSyrZTPP3ZKj3vCBcFkgs2/7ZrS40p+aIsH076mSm50HiyNICKR6Cm5kYFEUkPJDZFouPsvCKaXHm/9b9y9Lbz7JEFDYhGZCqrcEMmqF/bOYcXs18Gmti/GjGXHMZNWmreq/0IhauutotSGqK09tCwWgxIbpuNgRXSBiURIyY0MaFiKyLRyPfDTpPsOPGRmz5jZDePtZGY3mNl6M1u/b5/GJ4uMUOWGSNa0vt5Py8CxrDilb+oPbsaJsT0076mZ+mNL5LoGqqgr7z0sJ2YG9eU9dPRWRheYSISU3MiAhqWITA9mdiFBcuNTSYvPd/czgcuBG83sLan2dfd73H2Vu6+aPXt2DqIVmSZUuSGSNS/8eBcAp58Ty8rxT5zdSXPHrKwcW6IVH6ggVt4/ZnlDZS+dA9URRCQSPSU3MqDkhkj+M7MVwLeAK939QGK5u+8Jf+8F/gNYHU2EItOUkhsiWbPhsf0ArHhHdkZTnrh4iN1Dc+lrjWfl+BKd+GAlsYqBMcvrq/rpGMpOskwk3ym5kQENSxHJb2a2APh34IPu/nLS8piZ1SVuA5cAKWdcEZFx9IdXBisqoKoquK9sv8iU2PCc08QBjnvj4qwc/8TTqhimlJ0/35WV4093ZnaZmW0xs2YzuyXF+j9NmnHtV2a2LIo4x3AnPlRFbeXY5EZDdT+dw7U6T0tRUnIjA6rcEImWmf0AeAI42cxazOz68APHn4ab3AY0AXePmvL1GOBXZvY88DTwE3f/Wc6fgMh01t8PJSVQWhpUbgD09kYb0ygZfEGpNLMfhuufMrNF4fJyM/tu+OXlRTO7NdexS3F7oaWRFfU7sNLsfCQ/8ZwmAJqfUC+p0cysFPgawbDVZcA1KZIX33f30919JfD3wBdzHGZqAwN0U0uscuyXk/qaQTpoyLvztEgulEUdwHSg2VJEouXu16RZ/2HgwymWbwfOyFZcIkVhYCCo2oCgcgOgry9oy58Hkr6gXAy0AOvMbI27b07a7Hqgzd1PNLOrgc8Bfwi8F6h099PNrAbYbGY/cPeduX0WUox82NnUtYAPnfp01h5jyZvnArD9BQ1LSWE10Bx+VsDM7gOuBEbOHe7embR9jKBJefR6eogTo6l67JeThtgQW2gImkHnyXlaJFdUuZEBDUsREZGi1d8P5eXB7USSY2BsKXSERr6guHs/kPiCkuxK4Lvh7QeAi8zMCL6oxMysDKgG+oFORHJgz/P76PZaTl02tVPAJpszv5JK62P3Dl2hS2Eu8ErS/ZZw2WHM7EYz20ZQuXFzqgPlfMa1MLkRqx4es6qhdohO6tUfSYqSkhsZ0LAUEREpWsmVG4kkR//YDv0RyuQLysg27j4IdBAMZXsAiAOvAbuBf3D31lQPoumiZaq9+HALAKec25i1xzCDBdX72LVPs2ekkCqrNKYyw92/5u4nEMzE9pepDpTzGddGkhtjC0nq6z0YlpKYxlukiCi5kQElN0REpGjlf+VGJl9QxttmNTAEHA8sBv7czJakehBNFy1T7aUn2wE49dIFWX2chTM62dU5I6uPMU21APOT7s8D9kyw/X3Au7IaUaYSyY2ascmNhnrop5LedvXckOKj5EYGNCxFRESKVv5XbmTyBWVkm3AISgPQClwL/MzdB8Lpon8NrMp6xCLAiy9CvXVy7PKmrD7OwuMG2D14PHR1ZfVxpqF1wFIzW2xmFcDVwJrkDcxsadLddwJbcxjf+Hp7g+RG7dhV9Q1BLrdzf16dp0VyIqPkxtF2IQ/X3Rou32Jml6Y7ppndFC5zM5uVtPz9ZrYh/PmNmZ2RtG5n0jRNiVkSpowaioqISNHK/8qNtF9QwvvXhbevAh51dycYivI2C8SAc4GXchS3FLmXXq3j1LpXsey13ABgweJSXuc4el/amd0HmmbCIWo3AQ8CLwL3u/smM7vdzK4IN7vJzDaZ2XPAJzh0HonUcLyHHmqI1Y79KlffGCzrPJBX52mRnEg7W8pkupCH0yldDSwnKPl8xMxOCvcZ75i/Bn4MPD4qlB3AW929zcwuB+4Bzklaf6G77z+C554xDUsREZGiNTBwKLmRh5Ub7j5oZokvKKXAvYkvKMB6d18DfBv4FzNrJqjYuDrc/WvAPwMbCYau/LO7b8j5k5DiMzzMS91zufiUlqw/1MJlwYwZr6x7naVnn571x5tO3H0tsHbUstuSbv9ZzoPKwMH24BwcqxubGattDL7exduV3JDik8lUsGmnSQrv/1V4+wHgq2EX8iuB+9y9D9gRfqhYHW6X8pju/ttw2WFBuPtvku4+SVB2mhMaliIiIkWrv/9QxUZ+Vm5k8gWll2Da19H7dadaLpJtHS/sZo8v4tRTc5DcODMY9rL7hQ6WptlWpod4WyK5UTpmXSK50d2hq7JSfDIZljKZLuTj7ZvR1EsTuB74adJ9Bx4ys2fM7IbxdjqaTuee1KdHlRsiIlJ08rxyQ2Q62vLLvQCc8obsz2KyYHkdALte7sv6Y0luxDuDLyWx+hTJjZlBErq7Q1dlpfhkktyYTBfyI12ePhizCwmSG59KWny+u58JXA7caGZvSbXv0XQ6H06aPlrJDRERKTph5cbu3fCxb5xCP+V5V7khMt289Ew3AKe8aVaaLSdv3nzDGGbX7iw395CciYeJi1jD2CL8keRGZ0ZfrUQKSibJjcl0IR9v3yOdeonw2CuAbwFXuvuBxHJ33xP+3gv8B4eGvkyakhsiIlLUwsqNm2+Gf3xgLptYrsoNkUna+tIwJQyx5Lxjsv5YFRVwfFUbu/dlv0pEcqO7M/iCUjujfMy62qbKYJsuJTek+GSS3JhMF/I1wNXhbCqLgaXA0xke8zBmtgD4d+CD7v5y0vKYmdUlbgOXEDQGmxLJCQ313BARkaLT38/j3av4z/8M7nbQoMoNkUna3lLBgvLXKHywY/0AACAASURBVK/MaOLCSVs4s5NdXTMOv2on09bIsJQZFWPW1c6qAqC7O6chieSFtGfUDKdJ+jbQFDYM/QRwS7jvJuB+guajPwNudPeh8Y4JYGY3m1kLQTXHBjP7VvgYtxH08bh71JSvxwC/MrPnCRInP3H3n03i3+QwqtwQEZGiNjDAX+74YyqDi4FBckOVGyKTsq21kRMasjLJX0oLjhtk1/B8eP31nD2mZE88HvxOmdxINBSN5zIikfyQyWwpR92FPFx3B3BHJscMl98F3JVi+YeBD6dYvh04I+2TOEpKboiISFHr72dz9wIuuhjWroV2GlW5ITIZ7mw7eDx/sOTl9NtOkYWLS/jRM/MZ3r6ekuOPz9njSnbEu4MhJ6mSG9XVYAwTP5ibqiCRfKL/9WkkJzc0LEVERIqN9w/QOVjDggXBfVVuiExO57Z97GcWSxbnrifCwlOqGaCC1zfszdljSvaMVG7Ujm0Sawa1FqdbyQ0pQvpfn4YqN0REpJj19JUw5KXMmxfcV+WGyORs/1XQQ/+E03LX4HPBGTMA2PVCZ84eU7InfjBIasRiqdfHSnro7s2oQF+koCi5kUZyQkPJDRERKTZd/UGzjZkzoTY2HCQ3VLkhctS2P9sOwJJVM3P2mAtPCRIpu5v1t1sI4j3BV7jxkhu1pUpuSHFSciMNDUsRiZ6Z3Wtme80s5UxIFrjLzJrNbIOZnZm07joz2xr+XJdqfxEZX2dfkNyoq4PGBs2WIjJZ214MEgwnvOm4nD1mYljZrlf00b8QxHtLKWOAirEtNwCoLe2lu3/sNLEihU5nuDQ0LEUkL3wHuGyC9ZcTTDW9FLgB+DqAmc0EPgOcA6wGPmNmM7IaqUiB6RwIphWsr4eGBlflhsgkbdtVSlNJKw1zKnP2mPX10Fjeza69uRsKI9kT7y0lVtIz7vra8j66+8fJfIgUMCU30jg8uZG7xk8icoi7/wJonWCTK4HveeBJoNHMjgMuBR5291Z3bwMeZuIkiYiM0tkffBmqr4fGGabKDZFJ2r6vjiWx3+X8cRc2dLC7swFcn2enu3hfGbHS3nHX11b00T2Qu+SZSL5QciONw4al7G+PLhARmchc4JWk+y3hsvGWj2FmN5jZejNbv2/fvqwFKjLddA0eSm40NJoqN0QmaVvXMZzQlPvPlAuP6WXX0Dxoa8v5Y8vU6u4rJ1bWN+762ooBugerchiRSH5QciONwyo3+tR0QyRPjZ0LDXyC5WMXut/j7qvcfdXs2bOnNDiR6axzIKlyI5HcUOWGyFEZ7Blg19BcTpg7/hfTbFmwwNnFQti1K+ePLVMrPlAxcXKjaoD4kJIbUnyU3EhDs6WITAstwPyk+/OAPRMsF5FMDA/T6bVA2FC0UQ1FRSZj97rfMUQZS5bm/iP4wqWVdNJAx+ZXc/7YMrXigxXUlo9fQVdbNUT3UE0OIxLJD0pupHHYsJShVBeBRSQPrAH+KJw15Vygw91fAx4ELjGzGWEj0UvCZSKSiYEBOqkHEg1FoZ1GvE/DUkSOxranDwBwwmm5/+K5cEXwt7xrQ0fOH1umVnywkljFBMmN6iG6XckNKT6aADkNzZYiEj0z+wFwATDLzFoIZkApB3D3bwBrgXcAzcBB4P8L17Wa2WeBdeGhbnf3iRqTikiygQG6qKOsZIiqqlIaG2GQcnp6QB+bRY7c9o0HAVhydlPOH3vhaWFy46UeVuT80WUqxQermFfZNe762pph+qmkv59xp4sVKURKbqRxeHJDlRsiUXD3a9Ksd+DGcdbdC9ybjbhECl5YuVFf1Y9ZNQ0NweL27jIlN0SOwrZmp5Je5q46LuePvWBh8DlWLTemv/hwFbHK8ZvS1saC9mLxziEqZpXmKiyRyGlYShoaliIiIkWrv59O6qmrCnpsNDYGi9sP6lKgyNHY/moFi0tfoaQm980ejzkGKkv62f26/n6nu7jXEKsav6Q8Fgt+dx/IfeNakShllNwws8vMbIuZNZvZLSnWV5rZD8P1T5nZoqR1t4bLt5jZpemOaWY3hcvczGYlLTczuytct8HMzkxad52ZbQ1/rjvyf4bxJYailNPP0LCSGyIiUkQSlRvVwWxhicqNjoPlEQYlMn1t29/Ikrq9kTy2GSyoa2NXW30kjy9TJ+41xKrHT27U1gXfWbr39+YqJJG8kDa5YWalwNeAy4FlwDVmtmzUZtcDbe5+IvAl4HPhvsuAq4HlwGXA3WZWmuaYvwbeDowumrscWBr+3AB8PXyMmQTj788BVgOfCRsHTolE5UYF/QwNT7ytiIhIQenvp4s66muC5Ea+Vm5M8iLMCjN7wsw2mdkLZqb5EyUr3GFb/FhOmN0ZWQwLZh1kd/8xEI9HFoNMznDfAAeJEatOObM9ALX1wVe87lY1f5bikknlxmqg2d23u3s/cB9w5ahtrgS+G95+ALjIzCxcfp+797n7DoJmf6snOqa7/9bdd6aI40rgex54Emg0s+OAS4GH3b3V3duAhwkSKVMiObkxOKxRPCIiUkQSlRujkhsdvZURBnW4SV6EKQP+FfhTd19O0LhY89xKVhzYN0yX17Jk/mBkMSycO8QuFsLu3ZHFIJPT09oDHBp6kkoiuRFvU3JDiksm39bnAq8k3W8Jl6Xcxt0HgQ6gaYJ9MzlmpnFkfCwzu8HM1pvZ+n379qV5uEAiuVHOgIaliIhIcQl7btTHgvLnkYaivXlV3DCZizCXABvc/XkAdz/g7pobTbJiZBrYk6K7WLbghHJe43j6tiq5MV3FW4M+GhMmNxqDOSO625SrleKSydk11Tf60XVQ421zpMuPJo6Mj+Xu97j7KndfNXv27DQPFzh8WIqSGyIiUkTCyo26WPBmmI+VG0zuIsxJgJvZg2b2rJn9/+M9yNFcIBFJtm19GwAnnFEXWQwLl9cC0PKCZkWfrhJDTWK1438vqW0IZkjpbo+uSkgkCpkkN1qA+Un35wF7xtsmLPFsAFon2DeTY2Yax9EcK2PJlRsaliIiIkUl0XOjLngzrKqCchugvb864sAOM5mLMGXAm4D3h7/fbWYXpXqQo7lAIpJs+8aDACw655jIYliwIshQ7t6snhvTVWKoyYTJjZlBX6TuDhWiSXHJ5Nv6OmCpmS02swqCBqFrRm2zBkjMUnIV8Ki7e7j86rCR12KCZqBPZ3jM0dYAfxTOmnIu0OHurwEPApeY2Yywkegl4bIpcWi2lAGGlNwQEZEiMtQ7QDd11NcGuQIzaCyL095XE3Fkh5nsRZifu/t+dz8IrAXORCQLtjYbc2mh5tSFkcWwcElwRX/Xdn3pna5Gkhv1peNuM5Lc6NRsCFJc0n5bD8s3byJIGLwI3O/um8zsdjO7Itzs20CTmTUDnwBuCffdBNwPbAZ+Btzo7kPjHRPAzG42sxaCDycbzOxb4WOsBbYTNCX9JvA/w8doBT5LkDBZB9weLpsShw1LcQ1LERGR4pG46ldff6gQorEiTsdAXiU3JnMR5kFghZnVhEmPtxJ8ZhGZclv31LC0YndQAhWRefOC37v2lEUWg0xOvDMYajJRciM2Mxg62N2VbtS/SGHJ6Mzm7msJkgvJy25Lut0LvHecfe8A7sjkmOHyu4C7Uix34MZxHuNe4N4Jn8RR0mwpIiJSrDrDGSvrkloENJQfpH1ggk52Oebug2aWuGBSCtybuAgDrHf3NQQXYf4lvAjTSpAAwd3bzOyLBAkSB9a6+08ieSJS8JrbZ/GuphcjjaGyEo6rbmP3gfz5G5YjEw+TzommoamU11dTSa+SG1J0lLZN47DZUlzJDRERKR6dHcEH4/r6Q5WLteX9xPvyaraUyV6E+VeC6WBFsqajA/YNzGDp3J6oQ2HhzC52vToLBgagvDzqcOQIxTuD5EascYLXrqaGWrrpjqvqXIqLvq2ncfiwFP1ziYhI8ejqCn7XNxz6gBwr76d7KK8aiorkva0bg+k7T1wacSDAguMG2c0CePXVqEOJnJldZmZbzKzZzG5Jsf4TZrbZzDaY2X+bWXQNU0LxrsySGzHiSm5I0dG39TSSG4o6JSPJDhERkUKXGJZS33jo40KsYoD4cH5Vbojku61P7gdg6Yro+9UsXGzsZgHDO3ZFHUqkzKwU+BpwObAMuMbMlo3a7LfAKndfATwA/H1uoxwr3h1U1MVmVIy/UU0NdXTRdVBf9aS46H98GsnDUuBQskNERKTQdXYHV/3qxiQ3VLkhciSanwumXj3h3OinEV5wcg19VLFv876oQ4naaqDZ3be7ez9wH3Bl8gbu/lg4kxLAkwQTHkQq3h38TjQNTamkhPqSbroOjt90VKQQKbmRRvKwFFByQ0REikdnV/Axob7x0AfkWOUgcY/+6rPIdLJ1yzDz2U31qYuiDoWFZzQCsGtjV8SRRG4u8ErS/ZZw2XiuB36aaoWZ3WBm681s/b592U0axeNOKYNUNE58Hq4r66GzRz1VpLgouZHG6OTG4GCEwYgUqQzGxH7JzJ4Lf142s/akdUNJ60ZPDykiE+iKBx8T6mYc6j+u5IbIkdvaUsWJJdvhmGOiDoUFS4Mr/ruaByKOJHKpGlKknF7EzD4ArAI+n2q9u9/j7qvcfdXs2dmtzonHjRhxLDbxebi+vIfOvgmqO0QKkGZLSUPDUkSilTQm9mKCqyrrzGyNu29ObOPuH0/a/qPAG5IO0ePuK3MVr0gh6Q7HaydPORirGqKfSgYHoUyfIkQysvXATN5T/zRY9A0eF4YtMXe3RB9LxFqA+Un35wF7Rm9kZm8H/jfwVnfvy1Fs4+o+WEKMOFTUT7hdfUUfXb1KbkhxUeVGGkpuiEQu7ZjYUa4BfpCTyEQKXHdPKeX0U1F7qHFdbXVQwhiPRxWVyPTS2goH+utZOi/6aWABGhqgruwgu/YWfQXWOmCpmS02swrgauCwCk8zewPwT8AV7r43ghjHiPeUELODaRNl9VV9dParP5IUFyU30kgkMzQsRSQyGY+JDadoWww8mrS4KhwH+6SZvSt7YYoUnnhveIWw/NC47Vh1kPXv7lC2XyQTm54NLvafdnrEgYTMYGFDO7s7GsBTjsIoCu4+CNwEPAi8CNzv7pvM7HYzuyLc7PNALfBv+TK8Nd5XSqykN+129dUDdA1Va6ZHKSoqKE1DDUVFIpfxmFiCqy4PuHvyX+oCd99jZkuAR83sBXffNuZBzG4AbgBYsGDBZGMWKQjx3jJq6YbyhpFlsargjTHeMQioE79IOhsf2wfM47TzG6MOZcTCOT3sOjAXOjqgMX/iyjV3XwusHbXstqTbb895UGnE+8qIlR5Mu119zRBOCfE41NXlIDCRPKDKjTQ0LEUkchmNiQ1dzaghKe6+J/y9HXicw/txJG+Xs2ZgItNFd29pULlRkjQVbE2QW4y3F30zQpGMbHz6IA20M/fNS6IOZcSCec5uFsDu3VGHIkco3ldOrCx964/6WPClpbMz2xGJ5A8lN9LQbCkikUs7JhbAzE4GZgBPJC2bYWaV4e1ZwPnA5tH7ikhq8b5yYnZ4n4CR5Eansv0imXhhSzmnsQk7+aSoQxmxcGkFrTTRveXVqEORIxQfKKe2PIPkRl1wrlZyQ4qJkhtpqHJDJFoZjomFoJHofe6HDSA+FVhvZs8DjwF3Js+yIiITi/eXUVtyeOfQWCxc16lsv0g67rDx9Vmc1vgKVObPzBULlgfjFHZt6Ig4EjlS8YFKYhX9aberDydTUXJDiklGPTfM7DLgHwkG137L3e8ctb4S+B5wFnAA+EN33xmuuxW4HhgCbnb3Byc6ppktJpgNYSbwLPBBd+83sy8BF4YPWQPMcffGcJ8h4IVw3W53T/7CMymJZIaSGyLRSTcmNrz/Vyn2+w2QJy3cRKaf7r4KZo9qXHcouaEudSLpvPYatA3UcdrC7qhDOczCFUEfnd1belgecSxyZOJDlcQq0ieX6xqCa9idbUOoP5IUi7SVG2ZWCnwNuBxYBlxjZstGbXY90ObuJwJfAj4X7ruMoIR8OXAZcLeZlaY55ueAL7n7UqAtPDbu/nF3X+nuK4GvAP+e9Pg9iXVTmdgADUsREZHiFR8oJ1Y6alhKbbiuS8kNkXQ2PhPOlHJGfn25XLAo+Aqwa2fxzpYyXcWHqohVpv9CUt8YJjf2p6/yECkUmQxLWQ00u/t2d+8nqKq4ctQ2VwLfDW8/AFxkZhYuv8/d+9x9B9AcHi/lMcN93hYeg/CYqaZuvIZRTQOzRbOliIhIsYoPVFA7OrkRCyYw6lZyQyStjY/vA2D5m2dGHMnhjjsOymyQXa9VRB2KHIHhYTg4XEWsKv0XkvqmYArvzn3p+3OIFIpMkhtzgVeS7reEy1JuE46P7wCaJth3vOVNQHt4jJSPZWYLgcXAo0mLq8xsvZk9aWapkiGJfW8It1u/b9++8Z9xEvXcEBGRYtU9UDmmK39tffDRId6lK74i6Tz/xEGO5TVmv/mUqEM5TGkpLIgdYGer5gidTnp6wCkhVp0+uZxIbnS1amYrKR6ZJDcsxbLRn2jG22aqlie7GnjA3ZPTDAvcfRVwLfBlMzshxXGOaqpHVW6IiEixig+OTW7UhJUb8biSGyLpPL25jrPLn4OT8memlIQls7vYfvA4jbmeRuJhf+fErFUTqWsKqnI6W/X6SvHIJLnRAsxPuj8P2DPeNmZWBjQArRPsO97y/UBjeIzxHutqRg1Jcfc94e/twOPAGzJ4XhkZXbmh87+IiBSDwUHoG64YM+VgaXUFVfQQz6/+iCJ5p70dXuo4jnMW7QVLdf0uWkvmDbCdxUHXU5kWjiS5Ud4Yo5qDYUNRkeKQSXJjHbDUzBabWQVBcmHNqG3WANeFt68CHg2nY1wDXG1mleEsKEuBp8c7ZrjPY+ExCI/5n4kHMbOTgRnAE0nLZoSztWBms4DzgSmb6nHMbCkDGmMsIiKFb+RDdNmoZnTl5cSIEz+Y+5hEppN1jwd/RKvPy69moglLTixhP7PpeunVqEORDMW7g6RGrDaDZFltLfV00tmhKjspHmmTG2H/i5uAB4EXgfvdfZOZ3W5miZlJvg00mVkz8AnglnDfTcD9BMmGnwE3uvvQeMcMj/Up4BPhsZrCYydcQ9CgNPmv9FRgvZk9T5AYudPdpyy5MWZYSp9KN0REpPCNJDcqRo3XrqgIkhvx/LsSLZJPnvq/QUXE2e8a3aouPyw+LZjXecezbRFHIpmKtwaVdBklN2KxILnRqeSGFI+y9JuAu68F1o5adlvS7V7gvePsewdwRybHDJdvJ5hNJdWx/irFst8Ap0/4BCZhzFSwfSrtEhGRwtcdDjuprUhdudF9cEbugxqHmV0G/CNQCnzL3e8ctb4S+B5wFnAA+EN335m0fgHBhZi/cvd/yFXcUtiefnKIU3iRxredGXUoKS05K/gb3r6phxURxyKZibf1A1XE6jIovq+tpZ5WOruqsx6XSL7IZFhKURszW0q/khsiIlL4Rio3KkdVLCYqNw7mx0cIMysFvgZcDiwDrjGzZaM2ux5oc/cTgS8Bnxu1/kvAT7MdqxQPd3hqxxxWN7wMDQ1Rh5PSktODyo3t23Rlf7oIkhsQq88suVFHF53d+TksSiQb8uOTSR7TsBQRESlG4w5LSfTc6M2bjxCrgWZ33+7u/cB9wJWjtrkS+G54+wHgIrOgw2M4hfx2YBMiU2THS33s7Z/BOSt6og5lXDNmQENJJ9v3VEUdimQo3h6cj2sbMii+TwxLOajkhhSPvPlkkq9GNxTVsBQRESkGI8NSqke971VUUEs38Z68+QgxF3gl6X5LuCzlNmHfrw6CXmExgl5ff53uQczsBjNbb2br9+3bNyWBS+H66Ve3AfD2Dx4XcSTjM4PFtfvYcaA+6lAkQ/GO4CJrLJPkRnV1kNzoKc9yVCL5I28+meSrMcNSNFuKiIgUgXGHpYxUbmTUtisXUnXWG11nP942fw18yd3TTmzr7ve4+yp3XzV79uyjCFOKyY9/7Cy1rZz0wXOiDmVCS2Z1sj1+TNRhSIZGkhszKtJvXFJCfXkPnb0ZbCtSIJTcSCOR3Kgk6E480KvKDRERKXwjyY3qUUn9RM+NvrwpdW4B5ifdnwfsGW8bMysDGoBW4Bzg781sJ/Ax4H+Z2U3ZDlgKW3eX8+juE/m9JZuhKr+HfCyZ18+O4QUMd3RFHYpkIN4VnI8zSm4A9eW9dPbn9/9Bkamk5EYaieRGjOBTXl+PKjdERKTwJYalxKpGJfUTlRt9eVO5sQ5YamaLzawCuBpYM2qbNcB14e2rgEc98GZ3X+Tui4AvA3/r7l/NVeBSmP772zvpp5Lfe1f+DwdYckIJfVTx2rOvRR2KZKC7c5gShqhsyCxhUV/Vz8BwGX19WQ5MJE8ouZFGIrlRUxo0FO09qOSGiIgUvkTlRm1s1AiPsHKjuy8/vriFPTRuAh4EXgTud/dNZna7mV0RbvZtgh4bzcAngFuiiVaKwb9/bQ/1dPCmj62KOpS0Fi+vAWDHM60RRyKZiMedGHEsVpPR9vXVwbD6zs5sRiWSP/Lmsku+GkluVA7BQejt0XRZIiJS+BLJjZrqUe97YeXGwFApAwNQngc5DndfC6wdtey2pNu9wHvTHOOvshKcFJXNP97O/2k+h/+x8gkq5r056nDSWrJqJgDbN/XwpohjkfTi3WE1eXV1RtsnJzfUKkiKgSo30kjMlpL4cKfkhkg0zOwyM9tiZs1mNuaqq5l9yMz2mdlz4c+Hk9ZdZ2Zbw5/rRu8rImN1d0M1BympGHUdJKzcgEMJEBEBBgb48z9uo5ZuPvODU6OOJiMLV83GGGb7Nn2+nQ664kYt3VCTYeVGbXCVtqMjm1GJ5A9VbqSRqNyorjE4oOSGSBTMrBT4GnAxQWPAdWa2xt03j9r0h+5+06h9ZwKfAVYRzI7wTLhvWw5CF5m24nGCD9EVoxrXlZYeltxobIwgOJGIHTgA3/se7NwJ9Pcxq7eF//6PTn7ecRZf+P3HmXXKBRFHmJnKWBnzSl9l+6uaUWM66IyX0cABqJ6T0fazGoJh9fv3ZzMqkfyh5EYaw8NQwhDl1WUYw0puiERjNdDs7tsBzOw+4EpgdHIjlUuBh929Ndz3YeAy4AdZilWkICTGdo8Zd2JGrLQPhlS5IcXpq7e38hefraN3sJwG2nGMTk5gvr3CF97/LH/23QuiDvGILI7tY8f++qjDkAx0HCyjgY6MKzfmNAbDUn73u2xGJZI/lNxIY3gYShnCqquoopfe3qgjEilKc4FXku63EEzhONp7zOwtwMvAx939lXH2nTt6RzO7AbgBYMGCBVMUtsj01d05HCQ3UnyIri3rVXJDio739vFn5z7FV55/C++0n/B3K3/I6W+ZAXPn0nvKSireci4ljfPTHyjPLGnq4KHdJ0cdhmSgs6ecY6wz42ZHc5qC8fV792YzKpH8oeRGGkHlxjBUVwfJDU2lJBIFS7FsdBnVfwE/cPc+M/tT4LvA2zLcF3e/B7gHYNWqVSrRkqIX7xwed2x3rLwf+pTckCLS3s5ty/+dr+z5Yz6+4hH+Yc0KSha+c2R1ZhNz5qclc/vYs+NYeuLDVMfUji+fdfRWUl96MOPt62eWUUEfe/dWZjEqkfyR0Rksg0Z+lWb2w3D9U2a2KGndreHyLWZ2abpjhvPUPxU2/vthOGd9ZM0Ch4ZGJTdUuSEShRYg+XLYPGBP8gbufsDdE+nHbwJnZbqviIwV756gcqMyKHXu6sp1VCIRGB7mXy/8Nn+z54+5/qIdfOG5t1OycPpVaIxnyQnBNYCdzxyIOBJJp7OvkobyzJMbNqORY/gde/cMZjEqkfyRNrmR1MjvcmAZcI2ZLRu12fVAm7ufCHwJ+Fy47zLgamA5wRj3u82sNM0xPwd8yd2XAm3hsRN+6O4rw59vhY+RaBZ4DsG4/M+Y2Ywj/HcY15jKjd5UF4FFJMvWAUvD5GcFwXllTfIGZnZc0t0rgBfD2w8Cl5jZjPDccEm4TEQm0N0VTjkYi41ZV18TfFDu7Mx1VCK5t/1T/8T/eO4G3nLiq3z9p4uxAvsouHh5kMDcvr414khkIsPD0NlfRX3FEVxpPfZY5rCXvS392QtMJI9kUrkx0sjP3fuBRCO/ZFcSlIADPABcZGYWLr/P3fvcfQfQHB4v5THDfd4WHoPwmO9KE99Is8Bw9oNEs8ApMXZYSoG9o4lMA+4+CNxEkJR4Ebjf3TeZ2e1mdkW42c1mtsnMngduBj4U7tsKfJYgQbIOuD3RXFRExjcyW0qKyo2GWJDc0PSCUuiGd+zij75wBiVlpXzv4eMzbXUwrSw5K7gmuGOTxpnls+5ucEpoqDyC5MZxxwXJjdeHsxeYSB7JpOdGJo38RrZx90Ez6wCawuVPjto30cgv1TGbgPbwi8zo7WESzQLh6BoGDg85pQxBVdBQtKdP3aRFouDua4G1o5bdlnT7VuDWcfa9F7g3qwGKFJj4QQuHpTSNWVdfG3xQVuWGFLr/c/V/8Wu/iX/+3AEWLspshorp5pg3HE81B9nerC/A+Sxxvm2oOoIGgMceyxyeZ+N+9VKR4pDJ//RMmvGNt81ULYegWeAid18BPMKhSpGMmgVC0DDQ3Ve5+6rZs2en2mSM4aHhwys3+lW5ISIiha+7p2T8nhv1JRjDqtyQgtbzy/X876ev4Kzj9vBHHxub5CsU1tjAkpKdNL+ippP5LHG+TQwLzEhiWEp7Ba5W6VIEMkluZNKMb2QbMysDGoDWCfYdb/l+oDE8xmGPFVWzwOFBH5XcUOZTREQKmzvEe0vHTW6U1NZQXxJX5YYUtK999CVeYQGf/2YjJYX88c+MpbHX2LpvylrWTRsZTJrwFjN71swGzeyqKGJMGKncqBnIfKc5czjG9tI3WKYG0FIUMjlVp23kF95PzFJyFfCou3u4/OpwNpXFwFLg6fGOGe7zWHgMwmP+J0TX8LGkhQAAIABJREFULHBocHTlRiG/u4mIiAT9NoaHjUbaUzYUpaaG+pIuVW5IwRrasZu7nn8Lb1uwlQvfWZjDUZKdNKuV7fE5DA1FHUnuZDhpwm6CHl7fz210Y41UbsSO4EUqLWVOXdCjY+/eLAQlkmfSflPPsJHft4EmM2sGPgHcEu67Cbgf2Az8DLjR3YfGO2Z4rE8BnwiP1RQeGyJqFjimcmOgdKoOLSIikpfa24PfjbSnrNwgFqOBTiU3pGD95M8f5RUWcOP/aow6lJxYuqCPfq9g9+6oI8mptJMmuPtOd98ARN6QJHG+bag7slDmNAXJECU3pBhk0lA0k0Z+vcB7x9n3DuCOTI4ZLt9OcLIZvTySZoEjPTfChqJKboiISKFLm9yoqaHB2zUsRQpTdzd3/9c8jq9q5YrrM+vRNt2dtKwMfg4vP9PF4sV1UYeTK5lMmpCRo5m04EiNDEupP7LmGXOOLYEdSm5IcdAYizSGB8PZUlS5ISIiRSKTyo364XZVbkhBav78f/Dg4Nv5yAfilGV0GXD6W7o66Lex9akDEUeSUxlPSpDO0UxacKRGhqU0HNnkBnPmVQBKbkhxUHIjjeGhcFhKTY2SGyIiUhQOS25UVY3doKaGBm+jo0Pt96XADA/zjbv6KLNBPvzX89NvXyCOPXs+tXSxdUNv1KHkUlYnJZhqnR2OMUztrBTn5AnMXhT0Tfrd65GPrBHJOiU30hhK9NyorKSKPnoHiySFLyIiRWskuVHVR8ppImIxGuigU8kNKTA9P1rLP7e/m3etfo3jj486mtyxE5awlK28vL2oLuJlMmlC3ujY108dXZTMaDii/SrmzmYGrex9pS/9xiLTnJIbaYz03Cgro6qkX8kNEREpeCPJjZr+1BvEYtTTSUfnkZVHi+S7+2/bSCtN/M+/KaLMBkBVFSfVtLD19fqoI8mZTCZNMLOzzayFoLfgP5nZpvGPmF0d+wdooANmHOGUvccdxxz2sveVcc7nIgVE39TTGJktpayMqtJ+BgdKGRykaMZgiohI8WlrC343xgZSb1BTQwO76esz+vqgsjJ3sYlkzXPPcfdLF3LKnANccFFT1NHk3NI5nfzbzln090NFRdTR5EYGkyasIxiuErnOtsEgudF4hDP4HHssx/A79rx6bHYCK1Z9ffDaa7BoUdSRSBJVbqQxPBQ2FC0ro6o0+JDXp6ouEREpYO3tECvtpTw2zjecsHID0IwpUjCe+cv/4GnO4X/+eTVWhEVJJy0eYJhStm+POhJJpaPNg/PuUSQ3TmYLL+06sl4dMgF3uOYaWLECeouqT03eU3IjjZGGomVlVIfJDf0fFhGRQtbeDo3l3alnSoGwciNo3Z8PM6aY2WVmtsXMms3slhTrK83sh+H6p8xsUbj8YjN7xsxeCH+/LdexS57YvZuvr11ITVkff/SRcf7fF7hTTi8H4MV13RFHIv+PvfuOj6JOHzj+edILJXSQIkVAEREUEcUuKljQsx2evZxnO3v3d+rZy516nnrYOzY8bAciUlR6770TQw2kkb77/P6YATZhk2ySLdnkeb9e+8ru7JTnm5l5dvbZ78z4k5NLjU9LOYzl7MxNYseO0MTW4Hz4IYweDbm5MGtWpKMxPqy4UQXf4kZSnBU3jDHG1H9ZWZAWkwupqf5HcC8oCpHvuSEiscDrwFCgF3CZiPQqN9r1wG5VPQR4GXjeHb4TOE9VjwCuBj4OT9Smrtn95GuM1OFcfkkJTat3vcZ6o9fxTo+AJVOzIhyJ8Sc7N6ZmPTcaNaJX8gYAli8PflwNTl4e3HEHDBjgvP7118jGY8qw4kYVPL7FjdhSwIobxhhj6jenuJFTac+Nvael1IGeGwOANaq6TlWLgc+B88uNcz7woft8FHC6iIiqzlfVvbd+XAokiYhdQaSh2bqVN95PpoAUbn2gUaSjiZjU3l3oylqWLPBEOhTjR05+XM2uuSHCYQMaA7BsWQgCa2jmz0dzcnj/uLfY1PMM+O23SEdkfFhxowpeDz49N6y4YYwxpv7LyoI0yaq4uOHTc6MOFDfaA5t9Xqe7w/yO494hIRsof8XIi4D5qur3yloicqOIzBGROTusb3e9kvf4P3jZ81fOOWUPRx4Z6Wgi6JBD6M1Slq6zazPURdkFCU7ebVL9O9p0PK8vjchl+ezcEETWwMydy48M4bp/Hckfs97EO3U6lJZGOirjsuJGFcqclhLvVLKtuGFMeAVwPv3dIrJMRBaJyAQROdjnPY+ILHAfdfb+9cbUJVlZkKa7AypuRPq0FMDfpR+1OuOIyOE4p6r8paKFqOpbqtpfVfu3atWqRoGaOmjVKt56W8ikJY88U8FpWA1FYiK9W25h5c4WFNtdQ+uU4mIoLI2nSWIxxMZWe3o5YzCHsoJlM6y4UVs6dx7/F/c8yckwY1sXPthzMSxYEOmwjMuKG1Xwen3ulmLFDWPCLsDz6ecD/VW1D06X8xd83itQ1b7uY1hYgjYmymVlQZp3V7SclpIOdPR53QHIqGgcEYkDmgK73NcdgNHAVaq6NuTRmjply53P85T3IU4/sYjjjot0NJHXu3sxpRrHqlWRjsT42ltEbppSwe25q9K7N72S1rFsvfXKqa1vJqcxr7QPr78Og44p4gGep2TSlEiHZVxW3KiCnZZiTMRVeT69qk5S1Xz35QzqyD3pjYlGqm5xozQzoAuK1oHixmygu4h0EZEEYDhQvpfWdzgXDAW4GJioqioiacD/gIdUdWrYIjZ1gn7wITeOvYCCuMa8/rZdagWg9zHJACyZlV/FmCac9ubZJo28NZtBTAyH9VQyCpqTnVW+Y5sJ2J49jEw/ifaNc7jySrjt7kR20orFv9lFeOuKgIobNb3FmvveQ+7wlSJyVlXzdA9OZorIaneeCe7wiHQ79/gWN6znhjGREMj59L6uB8b6vE5yz5OfISIXhCJAY+qTvDzweqFZyfaKe24kJpIgpSTFlUT8tBT3Ghq3AeOA5cCXqrpURJ4Qkb29td4FWojIGuBuYO9xx23AIcDffI4jWoe5CSYCdOEi/vbnrfzAeTz7fCw9e0Y6orqhx6ntiaWUJb/sjHQoxsdOd3W0aFrzazv0OsVJbctGrwxGSA2SLljIVI7nlKNyiItjX2+vGcsaRzYws0+VxY3a3GLNHW84cDgwBHhDRGKrmOfzwMuq2h3Y7c4bItTt3Ov1ueZGglMtLSgI5hKMMVUI5Hx6Z0SRK4D+wIs+gzupan/gT8ArItKtgmntYoGmbisuDssHUJb7A1SaVnJaigikptIkobAu9NxAVceoag9V7aaqT7vDHlXV79znhap6iaoeoqoDVHWdO/wpVU31OYboq6rbI9kWE3obv5rFlces4OnSB7j+snxuv9M6Mu+VeHRverCKxfPtjil1SYZ7ot1BLWt+MZSjrusLwIzP1gUjpAZpw/jVbOEgBp3l3FWpUydom5zF9N87RTgys1cg2bzGt1hzh3+uqkWquh5Y487P7zzdaU5z54E7zwsgct3Oy5yW4hY3rOeGMWEVyPn0iMhg4BFgmO/dDvbe5tH9MjMZ6OdvIXaxQFPnDRkCzZrBOeeE9FyQ3budv2lUcrcUgJQUmsbnR7znhjHlqcKkjzZzz+AF/KHXCoYduoqLj1rL8KNWMqD5ag65tB9fllzAY7fv5u1PU4ix2sZ+HTrQL2Epc9Y2i3QkxseWLc7fg1rXvOdGhz7N6Zr0O7/MsFOwamrqJOfwctDZTQGnzn9c123MKOwLuXax1rogkHRem1usVTRtRcNbAFnuPCpaFtSw23l1fpkdNw66dIHlvzfef0HRROfHYituGBNWVZ5PLyL9gDdxChvbfYY3E5FE93lLYBBgd3k30cfjgenToW1bGDMGpoTu4mX7em5UVdxITaVp3J460XPDmL02TVjNqWnzOe3qjrwxoSerlnvYvHIPy+cXMm++0Dh3C/cdN5W1i/J5/F/NEH99AxsyEQZ2yiAjP4309EgHY/bKyIBYSmnVLq5W8zm5Txa/5fbFu3J1kCJrWKYsbU6TuD0c3nt/4hjYr4g1dGfn7PURjMzsFUhxoza3WAvW8P0LqkW38+r+MrthA+zISbSeG8ZEUIDn078INAK+KnftncOAOSKyEJgEPKeqVtww0Wf9eufD5/bbndfLQrcZlyluVHRBUXBOS4nJs+KGqTPmvjadYwc3Yn5uN/594SR2L9/G0uIezC86nKUZzVmV2ZIJBcfzzLRT6HhEWqTDrbOO7efckWPmNDs1pa7ISPfShm3ENm9aq/mcfGkbdtGCJSPs7h7VVlrK1F2HclyH9DJ34z3uNOdHgBk/WTfGuiCQ8l91brGWXu4Wa5VN62/4TiBNROLcLzRlluXT7fzkirqdi8hknG7ntbqdW5r7mefVmP3FDeu5YUxEqOoYYEy5YY/6PB9cwXTTgCNCG50xYbBkifP3xBOhXbvwFTeqOC2lWUw2y3aHLBRjArb8x42ccfuhNInP5+fxxRx+8qllR2jXLjKBRaG+53Yg8atCZvyQzUWXtol0OAbYsrmUg8jY/wWlhk6+qCXcC7+M2UOfl4MUXAORNXs1S7UXfzx6UZnhR5/TljhKmD5DODdCsZn9Aum5UeNbrLnDh7t3U+kCdAdmVTRPd5pJ7jxw5/kthL/beTOfUw2tuGGMMSai9hY3DjsMevWqG8WN1FTax26zrusm4rau3cOQYfEkUMykn70cfnLLSIcU1RJOHUQ/5jNzas2v72CCKyNDg1Lc6NwZDm6UyS/rOjinO5qATR+9FSWGQUOblBme0roRveNWMmd17XrVmOCosrhRm1usqepS4EucYsOPwK2q6qlonu68HgDudufVwp03hLnbuW/uiMEL8fHEJcQQg8eKG8YYY8Jr6VLo0gVNbUTpob2d4ob6vWlQre0tbjQlu8qeGx0lndzckF7f1JhKlZYolx23nh0laYx5fQNdTupY9USmch07MrDxMuZsbElJSaSDMQAZW2Nox5ayv77W0Cl9dzO59AS8S5cHIbKGY+oUJZZSBlx0YI7p33I9c7Z3DNXHsqmGgK4PXdNbrLnvPe1O11NVx1Y2T3f4Onceh7jzLHKHD1bVNuVv+aqq01T1CFU90v27txhSKwcUN+LikIR4kqTIihvGGGPCShcv4Rbva7RqBS3efZ6teamEqsvEzp3QJKWEODxV9tzoqJsA2LQpJKEYU6VHz5jO5B29eXP4ZI66+dhIh1NvDDyygAJPIosX2be1SCsuhp1Z8UHpuQEweFgqmbRk/ld2UdHqmLKiJf2SV5KaFn/Ae8d03cWu0qZs2BD+uExZdvOrCiQk7D+m23u3FOLirLhhjDEmvIqLmb8imf9sPJujjoKcwkQ+5OqQnZqyYQN0bpnnvKjsgqIpKXQqdX7L2Ly54tGMCZUfHp/Ds78cz5+7TeTKkUMjHU69csK5zpfoCV/ujHAkZutW52/QihtXtAVg/Fg7LSVQJSUwa3d3BnX2/6NC/37O/3LOr/nhDMv4YcWNisycSbOSbcD+nhvEx5NEIYUFVsU2xhgTJqtX84lnOAlxHr74Ak44toT3uA5dGprixrp10LWFe9X3qnpuFDvX7rbihgm3DRPWctUT3eiXvJxXZw3E7ukaXO2HHc2RLGDMt3bdjUjLcG+tEKzTUtq2E45ovJ6fV7Sv9bwaivkTd1NAMoOOKfb7fu/TWpNAEXN+2hXmyEx5VtyoSFwcaSU7AJ/iRv/+JHnzKdy8I8LBGWOMaSg8i5fxGZdx9ol5NGsG198Uzyp6MmVS8E+GV3WLG2nuAVoV19xoV7CO2FgrbpjwKtq6m0vOycdLDF+NbUxS80q2U1Mzhx7K2am/MmVVK7umToRt2eL8DVbPDYDBvbcxZU8/CrZkBWV+9d3UUc5KGHRuc7/vJwzoy5EsZPbscEZl/LHiRkU6dKAZzv3tYvBCbCxccQVJsSUULl0T4eCMMcY0FBN/yGcr7bj8hmQALrkEGsfu4YOZhwV9Wdu2QUEBdGnidkWvoudGbFE+7durXXPDhI2WlHJH/6nMKTqCD5/JoNvJHSIdUv0kwtlnlFCqcYz/X1Gko2nQ9vbcOCg1p/JTBavhjPOSKCKJX9+1624E4tdflS6s46Bz+vkfoX17+ictZe6G5ni94Y3NlGXFjYq0akWaON1yY1CIiYGUFJq2TWFXRiGssQKHMcY0SLvC2+101NR2NJI9nHthAuAc257ddQVjdh6DtzS4R1Hr3MuBd012fypMTq54ZPcgu+NBXuu5YcJDlSdP+JE3fz+XB85ezPkPBr/AZ/YbeFNfmrGLMe9tjXQoDVpGBsSKh1aHtQza6Vcn39CdZPL5frTdDqcqJSUwce3BDG61qOKCvwgDD9lJTkkKCxaENz5TlhU3KhITQ7MUp1IdG7P/4LHjUa3YTEf48stIRWaMMSZSpkyBNm3gzTfDsjhVGJN+BGe0W0JS0v7hQwblsVXbsujHjKAub19xI3cRdOrkFPYrsre40abIihsm5IqLlHuPmcxjs87lmsNn88z3R0Q6pHov7rSTGJowgW9/a2EX04+gLVugrWwnptehQZtnSqtUhjSfzejFh1hPgyrMmlJMjqcRZx1b+Sk8Q04tQvDy/Wi7Tk0kWXGjEmlNnL09xqdI2rFHMul0RFeuilBUxvjn9UKfPvDhh5GOxJh6yuOB22+H0lJ4/HHID/1V0ZfOLSS9tB1nH1X2l9MhlzYBYOwXwT0Zfm9xo3PGNOjZs/KRu3YFoGNyJunp2AGyqZnSUucWPevWOedElZObCx+8kc9RbdL559xTufmIKbw9v3+ldTcTJPHxXHvyenYVN+K/n/u/kKIJvbUrS+no3QCHBben0oUnbCOjpDWzf7E7fFRm3MfbicHDaZe2rHS81if04Him8d0o21ciyT4aKtHMvWZMTMz+u6N07AiFJLFzhd0ay9Qt6emweDFMnBjpSIyJInv2wPffO4WLqrz/PsyfD3/9q3NvvtdfD3l4Yz/JBGDouWU/rtue1IN+zOPH3xoFdXnr1kH79krSmiVVFzd69QKgk26gqAh22LW2TSDy8px96aKL2N31aKYmnc6HXR7jjW7/4OWUR3i27b94vP8P/OXUlQzsuYvmaR6uvTUFzc7h2yu+4o2Fg4iLtzujhMtp9/enK2t589nMSIfSIJWUwOy5wgBmBb24cc5VLYmjhNEjgnDaUVER/PCDc+GmeuanCTEMYBbNzhpQ+Yj9+jGM75i3IoV0/3eMNWFgxY1KpLWKB8r13Ojo/N282vrnmbpl9eqyf41pkLxeAu4/vXw5DBgAw4YFVqh4/XVWH34BPw79FwsG/gX++c/AiiK1MOanWPqwkPYnHVL2jdRUhjSbydRNHYJ6J4N166Brh2Ln5/KqihsdOkCjRnTMWwHYHVNM5Qp2FfDddd9wV6tPOO26g2n3zRs0Xz+XEzy/cA0fcitvcDcv8fC2O/j73HMZNbkliasWc5/3BX496k6WTM1h2MeX2C1fwyzm9FO58aAf+HVVO5Yvte5Z4bZwIRQUxTKIqUEvbjQ7awCnySS++qkppbU4k6Jg3K980/kO7j5vFaO63kfJ2x8ELcZI27ULZm9qw1nN50Dr1pWP3K0bw1ImAPDddyEMqqAAnnwSzjkHfvophAuKTlbcqESzds6F1Mr33ADYvDsVcnIiEZYxfoW0uPHUU3D00SGYsWlQdu1yLiIRCK8XXnoJRo92fhGqSmkpXHCBcwHMVq1g7dqqxz/3XLw7Mintdww8+mjlXQ/WreOHBe05dNnXDD1b6DdjBJ9uOx2mTg2sPTWwaxdMWdmKoTE/wSGHHPD+eX024dFYRo8O3jKd28A6dwqrsrghAr160XH7XAC7Y0o9lZcXeL3Qn11binj4rLl0aFnA+e9fwIjia8nvdQxDr2rNiy86P/auWuV0hsrKcpblLfGQuWInv8xvyjO/X82Jc19Bjj8ueI0ygRPh2v/rQDL5PPoXu7BouO39iDk+fs6+UwGDplEjbuoxiXVZLfj4w0oKVwsXwscf+/2MnPLGInoP7cAfto7g1Zg7uST/Iw6/8XjWffBrcGONkE/fzMNLLMOGBnDh1ZgYel58BL1lCf94vpTc3BAElJHByl5/4J5HU7hr4nmMPOsD9L77Q7Cg6GXFjUqkdXC6+/qe17m3uLGKHvQ9Jo7/Df8Yfv45AtEZ47j7brjrrv1FjZ07nQPEoBo9GubNg40bgzzjekyVoP6kXtds2uQUHwK97sR770GLFnDFFYEVhl96Ce65By68EHr3dr5hVTH/cd8W8M++H/NOydUU3X5fpaN7vx7NQ+tuICEzg/j5s7g4513yH3qywvFnvTyVS/mSfr1LmDIFTj2plGv4gHEvLa26LTX00UdQ6o3lsq4zIT7+gPcHnpJED1by/rvB6T1SWAi//w5dE9z+tD16VD1Rr1702PQziYnw229BCcNEmirrP5nKHUf/RufU7TRu7NQMWybncUmfFXx86wxyJs6p8oOmMLeEf1w6k27tC3nup36c3HwJ4/6xmKz8RGYsbcx77wv33uv8+Ni9u3Od3qZNITERJC7WKa717QsHHRSmhpuKtL5hGA+ljWDU1IOY+KNdTyCcpk2DTsnb6dAjBeLigj7/Cx7twzHM4vEHCw74HaF4ziK+7P4I1/edw3FXHcIFbabxt6P/x5hR+fzwA1x22jZOurU3GhfP9x9nkbcnhtGfF5EZ25oTru/B4l/Ce2exYFOFN/5VzABmctT9gwOaRv7+OP+J/SsbNsVwzz3BjadoWxb3Hjmewzf8wGvxd/F27F+4nJGc9Y/BbP3Evovuo6oN8nH00UdrVSa/vkRB9c+pn+4b5vGoxsd59GhmK6j+iU+05JTBOm6cqtdb5SyNCYqSEtVt21RLS1WbNlVt0kT17LNVnVSsOmtWEBaydavqxo2q2dmqMTHOjD/+OODJgTlaB/b1mjwCyQ+Vys5WPecc1fh41VdeiUxyKClxElYgvF5noxk7NrBYJ05UbdnS2SYGDVLdvbvy8ceM0bUxh+joDrfpuJghWtTvWCe+iixcqHPijtWrOk7UPx63UV/hdi2994EKRy/JzNa7kv+zb/sH1YFM098/+tnv+J5Sr17d4nsF1T9e6tHbb1cVPDpQpmvW8owDxt+9W/XghAztnJCuW7c6w7KzVY9ovF5bxezQbVsC/D9Xg9eremhPjx4bN1v1yiv9j/Tf/+ozPKigumZN7Zf5v/85/7tvhr2rmpwc2PbzwguqoOcNKdKOHQPf1KM5P2gwckRd5PXqzH/+ppc0/VFjKNU4ivXCxuP06VYv6zNNntFr4j7Wg0hXUE0iXy9klH7U8i7ddNYN6v37E6rffqveWbN1zSfT9ZnBE7RD7O8KqkObTtWFI6bZQVKUK/h+vHZhrfZsvk137Qr98qI5RwQrP3i9qu3bq17W6DvViy8OyjwPUFqq4ztdp6D6hwu8unGj6srZ2fq343/WNmxRUG2eUqCnHJWthzf/XWMp2fc5m0quPtRihOYs3lBmlktGLdeDSNe0uByd9ktxjUPzFhTqwmd+0K9Pe01n/uVdLZq7uLatrZYJE5x2ftDx/6o34R136P08r6A6rMsiXfXiN6qFhVVP5/Fo4W+zdPmTX+na18dqQeYeVXW2g4mjMrVf8nIF1RvP2azbtjkf0W/8q1hTJF8PjtmoS38+8Pgl6Lxe1UWLVEeOVN2+PfTLq0Bl+SHiCSBSj0ASz4JxWxVU/9L40zLDu3T27Nux2/G7vhXzFwXVsXeM1QWvTtZ3361y1sbUyqOPqjZurPrTT7pvW0xIUO3Z03n+6adVz8Mvj2f/Aehxx6kefPD+bzyg+pe/BDyrBntgkpOjU7pdpZfIl3pKkzl6EV/p90Ne09KSmh3Yl7z/sS7vMFjXHn+FFn9Q9YotnTBZf252sX7NH3Ra+4vVu3xF5RNs3aqLel6sb3O9fszlmjH8LtXiig9G9ixeq0/FP64tYzM1NaFI+zJff+x9j1Np86Ng8w59KPlljZfifZvR4SzWaX8d6Xd8T3Gp/r3dfzSWEk1r6tEuXdQtVkzXjeNXHjB+SYnqJT0WKKje/setunu36pcjizVV8rRz3CbduOrAA4q7LtqgoPr4ObP2be7/fWOLxlGspx+8SouK9o/r9apedm6OxlKi02/6oMx8Fj/znSZQqOefmBn0722//OK0+z2uUR03zv9IWVmannyIxohHH3mk9su84ALV1q1Vi4aer9qnT2AT/fCDKuiH/7dKQXXmzMAmi+b8oLXNEcGSn695k2bpkn+M1fmfLtXczKKqp/EjJ0f1/cfW6wlNFymoNo3J1geGLtT0NQUHjOvZna3TPl6jfz13rbZplLtvn04hT9uzWZuQtW/YqWnz9OenrKhRn0w8+0VNoFCP675Dc3NDu6xozhHByg/r1zv70r/lNtW//S0o8/Trs8/0ee7TBCnat/8KHj23wzwd+0V2mTp39rjp+uugB3XGoVdp5jV3a0UbwvqX/quHsErjY0r0lqvzdPp01dydhap5eVUWzosLSvXta37TrrHry/xo0YFN+s/zJmlOVvB/UCjP61U9Y2COtmCHFjz3SvUmzsrS4jvv02cP/o82IkfjKdK7Wn+iu+atr3Bh016arheljddU9ufVWEr04JRt2iYlW0G1DVv1u0dnHzD5nC/XalvZoqkxe/TlZ/IDqqMcICNDl9/6b32z/d/1oUav6os93tJv75qkv28s0aIi1fR01Q/uWaRXpH2vh7NYu7JGz+AnfeWYT3Tr8jBUO8updXEDGAKsBNYAD/p5PxH4wn1/JtDZ572H3OErgbOqmifQxZ3HaneeCTVdRmWPQBLPxrVOdfLmpmW/UJx0kpbZ2TqzTkH1cvlEB8Y5PToW3/y6Xtpzgd51l6oWFOiiyZlacOBxgjFVWrFCNT9fde5c1d69nR/YDzrI2fZgAGfpAAAgAElEQVQOO6zstnj77aoiqo8/Xo0FbNzofNiUlqqefLLqpZeqzpu3f6ZHHaUaG+ts+L16BTzbYB+YhCIPVfSo6YHJtq1evbDDTAXVVk0L9cQTvdomJUdBtVPTLH3yCY9mBFhYz8vx6FOD/qft2bxvVbRns74wdILm5R04vsej+t6tc7Qba8psE0fFLtAfXl7l9/vFomm5OqTxlDLjJ1Kgt3b5n27POLBnxcxppdotyYnnnNPy9e67Vbu1dj50hx+57IAOHJs2qR7Tcq2C6tXDdumsWapffO7VjknbNZYS/edDO8rEtWeP6h/7rXTy6Qnrddcu5wBj5IhsbSLZ2iZuh06fvP9TOy9PdfhZuxRU/zHwyzLLnv3qNG3Kbu3afJcudn/sKSlRffg+p8hye5P31bsnv8w0Hw54zWnbmUW6Y4ezS9x8s/N/eSL+CdXffy/bwKws/WfSwwqqjzwcvC9whYVOh5imcbm6p2PPyg8Eb75Zh8V8p6kpXl22rObLzMhwdvP771fV7t1VL7kksAnXrVMF3fXKhxofr3rffYFNFs35QWuRI4KhdO4C/fG81/Ty+M81mT1lvoz0brJR7zl7qf7836wKD3A9HtXly1XfebNEh5+SockxhQqq3WLW6ssX/qo5mYH90urxqM6fr/rvf6vecXORXn/eNr3tnLX65u1LdNW0HUFssakzcnP1616PaAyl2q3lbv15XGnIalfhKm7UJndU9AhWfvjTn1TjYkp1dUwPrVWCr4rXq/ruu7r0iD/qP5s9oe+c+IGu/W5JrWe7/ZFX9Cbe0Dj2/7jRlTV6ftJYfXzQTzrvx21ltp/8PV798P4l2i1ho4LqsY0W6zt3LdHZMz36xdvZekpLtwAbl6sP3ZajW7bUOsQKvfC0E/OrqQ/WqofClvRSveH0dSp4tIXs1BevXKiZmc57RYVeHfv8Qj0lbZ7TQyZml958yjL96MWt+v59S/VvR/9Pr0z6Uq+PfU/f6vkPLfjtwMLGXptG/qZDZYzToyauQM9qt1Dv6vxfHXHEa/rbTZ9o4coNfqfLXZGu753+iR4v0/YXVaS0zHGh76NVzE4d1medXnbGDj20udMJII5iPbPban3uxrX69dPLdNa7i3Tb1NXqLa6kh24t1aq4AcQCa4GuQAKwEOhVbpxbgBHu8+HAF+7zXu74iW7RYq07vwrnCXwJDHefjwBurskyqmpXIIknO9v5D93arGxx4/LLneEXMmrfym5ETpmd9zCW7nv+aNs3FVQvODVLN42eo8+c/Ztm/l6gGxfs0okvL1BvSalu21SomePnqpaUaG6uqidj6/5fOwoK7JePeqCwcH+vtA0bnC9y+fmqo0c7f+fMcc5g2LNH9ZZbnMf48c4ZIWecoXrCCc721Lat+yU00fnbs6dXD+3p9CZ6+y2vdmpfqpdf7i7wl1+cmefkODNfvdopv55/vupbb6nOmOF0P+/bV/WZZ/Znr169nOFt2jiv+/ff//7OnQG1N5gHJqHIQ5Utr7oHJvn5qq/8s1RbJudqIgX69OCJ+woQxYUeHTXoJR3MT86HgJToyWkL9L6jf9av7p+lG1cXldm9MzNVX3qmQNslO1/az+y0TN97u1TfebNET2uzWEG1XfIuff6JQl22zPll56MPvdr/4G3Oqkpdql+8k6MLFqi+/dRWPSTOKb6e0W+HjhrlbAK//aZ63cXZGkuJNmenPnP1Cl2zRnXBAtU/H79Y4yjWtPg8feTeAp02zTkL5bqrijVOSrQTG3Tiw+P3xVtY4NUnDhupsZRo26b5+uSTzjb98ENebZRQpI3J1tEXfVTm/5W9eKNeFP+tgurRvQv1tdecsxvat8hXUH2hx9vq9ZTNeUvfmKydcQ4Q/nhxiT70kGqXjsUqePS5Rk+qv37SM068V1uyXeNiPXr++apH9nH2k+t4R0vH+TllZcECfT3mNk2QIk1L8+7b/O/jefU8/azfde8d8abewFsKqvfe63xu1EZhoerVVzvLHclw1SeeqHyCpUv1d9pp69RcPeywA+svgSgtVf3rX51lrho526lyBPorocfj5Iq77tKhQ1W7dAns4yqa84MG8ctLoArWb9HJ936v97b9WNvhnO6RlpCnfzlznX727Hr96q6p+kT/b3RwwmRNwClWxFOkvVPX6Zkdl+mFh6/Qcw5fr8d03KLNkvYXRFqyXW9Kek+n3fCuendnhbVNJkrl5enEAQ9oN1Y7BfRO2/Wlv+fo8uXBPVQNR3GjNrmjskcw8sPnnzv76N/lMdVbb631/CJm5UrNuOM5/fa8t/XJ0yfppUcs08MabdQYSvcdzww5bIOe1nmtNo5xei0cGb9Uv7/vlwOOA9Tr1ZkPfK0Xx3ytgvN53jpupx6bOF//mPyNPtjhYx0x+Cv98Zm5unxOnt8fgqpSWKj63N/yNFZK9SK+Uu/3PwTl37Dgf+k6uMkMJzdLsXZM3ampMU4uPigmQ1/6w6/+e995PJWfxuvD+9N4/fHYR/WWuDe1b8ISTYnJ35frU8jTs1rP06cunq8j7l+jT1+6QC9oP3Nfgbxn0wx94YGdunats8jdmR6d8uyv+uoxH+nTLV/SVw4boQse+VI9+WWr5stGLdX7Dv6izPfevY+2ZOgf2k7V16+aoavm5oQtP4jzfsVE5DjgcVU9y339EICqPuszzjh3nOkiEgdsBVoBD/qOu3c8d7ID5gk8B+wA2qpqqe+yq7sMVZ1eWbv69++vc+bMqbTtqhAfU8otLb7k1Z1/2jf84Yfh2WdhLEO4vsU3ZGQm8QFXcw0fkhaXy3mlo/mYqzg0cT27i5LZRltaSCaZ2oIkCigkmfZxW9ld2ph8UhmQvIj5BYcSTwmDUuYzIf84erCKg1Ky+bWgP2fpOHYkdeR3ac+pOolF0oeE1Hh6exYxp6g3nVvuIW1POssKutKn4y6yd3nYWdSYIzpls357KrGlxRx8UAmrdjajDdtIaZ7MxuymdI3ZQF5yK7Lz4+lSvIotSV2ITYyjjSeDjZ4ONG9URFJBFls8rWjfLJ/83cXk0Yj2LYvYkRlDTCy0bFJCRrqHppJLYrtm7MhNonV8FiXJTSgohOYx2WyjNTGlxbQkk20xbWmSWERMSTFbSlrSvlEWnoISCmJSaZFaSGZWLBIfR1pKMdt2xdMkuYT4xBi274qjTWoehXGNyMuDNsk57JIWSEkxzRPy2KXNSPXkEOspJjOmFS0Tc8kriCVXmtAucRfbshMhMZFWyXls2NmIFk1KSIhTNmY24uBmOWzMa0FGVjLHtt1EelErYkoK6dQkm2W5HWkTu5OU+BKW53XksEabyCxMZUtJS/o2Xc+SzHYQn0CPRhlM3dKFg5vl0igmnylbunJChw2sz27O2qwWnNlpJRN/74F4PRzVajMTt/SibUImKQmlrMtrw8FJW8koakGJxtM8LptdpU0BiMFDs7hcMkvTADg1ZSaT8o+lWWw298a/yiOFf+Ompp8Rk72bN7iFyZ2u4slNV5Gb2o6Zrc6FDRuc2zWKOPdpTE6Gxo2dK16rOq+bN3cuU+/xwODBsH07LFoE117rTPvkk84VSy+8EE48Eb75Bs4/v9J9x90X56pq/ypHDEAo8lBlOSKQ/HDxxbBlC+iOncxf25hCbyKnMYFXL5jE4aP+DrGx+0cuLYUvvmD1mxN5Z80pTM7px4I93SkmEYCW8Vm0abSHAk8C63NaoMRwAr/xwk3rOe6NK/ff+tDjYeoN7/PwB935lZPLxNONNTza5xuumHIzMY1T9w0v2ZjB68d9wrNbrmY7bfYNT2EP1yR+zhOfdKXFxaeWmdeyO9/igX+1Ywxn48VpRxKF3MA7PPGYh2aP3V72doyZmcw+/UH+b+HF/MRZ+wZfyhc8PWQKh4x+EZKSyixD583ngxPf5ZmCO1mjzp1AjmEW/+z2BidOenL/1Zt97L7nKV58KYZX5Q6KSOQwVvB60j2c+PWdMHTogSvp99/ZOeQKHlhyBVMTT6eRN4e7Sp7n8lvSKr7169dfs+jSp3gh4RESUhM4NfMrruw527lafGLigeN7vXhOOpW/TL2Kd7meZol7OO6gTXRulk1ivIeEWA/xMV7nox5x/zq/s8P+5yXeGLbtacT4dd3YWdCIR2Oe4u9NX4IlS6q+oOKwYUz4fg9D5UcQ4azOK+ncPIekeA8xKCIQI4qgxIiiCF51HlmFSUxP78ii7W25sscMPtp8mvO/nzQp8As53nIL9OnDdwfdxMKFcP/9/v9VvqI5P0AAOWLePCa/PJ8J67viVVAv+/6qus8VvF5BVd2/4PEKe0oSyClOIqcwnpz8eHbkp7LZ0w4vscRRwjm9N3HlPa0597LGB/6fvV7yJs9h0kebmTo3iaWbm7AtL4V8TyIJFNOKHXSO3cyx7TZz/HFKz8v7I2ee4XwWGBMoVfI//pqP7l/MiG1/YCF9AUiLzaV7k620aVxAm8Z7aJxYQmws+x8nn8D5f4ihfwB7fjBzRCXLqHHu0Eq+QAVyDDH5xpFMWH4QXi9uPnbyQVZhEgu3tmH29oM5innMaHoW8WuWQ8uWQWhx3bFz7kZG3/ULv85KYklRd1LI57AW2/njlYmc/vRpxKQkVTzx6tWsfmM8o8Y1Zl1+GzYUtmNDfms25jWnRMtefLtZTDaNY/NJji0mKa6EpLhSkuNKSYr3kJzgISnBQ1JMCUWlMWzd05i5Ow8mu7QR5/MNHz64gqbPPhi8RhcXs/Dhz/lspLJtVwKNWiRy+jlJDH3hVBLTgp+DvV5IT4d5Y7cx8Z21/DyvBcu9+++C1jlmI+f23sjwv3Xn+Iva1e4u25s3kzl7HRu3JLB5eyLrV5cybx78uqYdGz3O8Vy7+B0c3mIbrRvn0zi5lMbJHhLivIh7nNKn9VYuOWGL852jCpXlh0CKGxcDQ1T1Bvf1lcCxqnqbzzhL3HHS3ddrgWNxChkzVPUTd/i7wFh3sgPm6TP+Ie7wjsBYVe1d3WWo6ig/bbkRuNF92ROnG1pVWgI7AxivLrM21A31oQ0QeDsOVtVWwVhgKPJQ+RxRw/ywVyTXrS274S2/Piw7qvKD+56/HBHp7TAYrA2RF+3xQ/DbELQcUZHa5A5V3VluXrU5hqjr6sP2GYiG0k6I/rZWmB8CuaeQvzpO+YpIReNUNNzfLWgrG78myzhwoOpbwFv+3quIiMwJdeU41KwNdUN9aANErB2hyENlB9QgP+xbcATXrS07Mhpq2yP9f69AyPMD+M8RdfT/US3WhsiL9vghattQm9xRdkAtjiHquihdt9XWUNoJ9but/ooM5aUDvv2DOwAZFY3jdtlqCuyqZNqKhu8E0tx5lF9WdZdhjKk/QpGHjDH1g+UHY0xN1CZ3GGPqoECKG7OB7iLSRUQScC6m8125cb4DrnafXwxMdM9F+w4YLiKJItIF6A7Mqmie7jST3HngzvPbGi7DGFN/hCIPGWPqB8sPxpiaqE3uMMbUQVWeluJe2PM2YBzOVYXfU9WlIvIEzpVKvwPeBT4WkTU41czh7rRLReRLYBlQCtyqqh4Af/N0F/kA8LmIPAXMd+dNTZYRJPWhi5m1oW6oD22ACLQjVHkoiCK5bm3ZDW/5DXXZfkU4P9S5/0cNWBsiL9rjhyhsQ21yRwMTdeu2hhpKO6Eet7XKC4oaY4wxxhhjjDHG1GWBnJZijDHGGGOMMcYYU2dZccMYY4wxxhhjjDFRzYobFRCRISKyUkTWiMiDkY6nPBHZICKLRWSBiMxxhzUXkfEistr928wdLiLyqtuWRSJylM98rnbHXy0iV1e0vCDG/Z6IbHfvG753WNDiFpGj3f/LGndaf7fwCkUbHheR3931sUBEzvZ57yE3npUicpbPcL/bmHthq5lu275wL3IV7DZ0FJFJIrJcRJaKyB3u8KhaF+FWVV4Q56KEX7jvzxSRzkFart/1VW6cU0Qk22cbfDQYy3bnfUC+Kfd+hdtHLZfb06c9C0QkR0TuLDdOUNtdnRzlZ9pa5dMKlv2iiKxw/6+jRSStgmkrXUc1XHaFea3ctHX68zLYRCRWROaLyA/u65Dn7GDyt60Euo3XBSKSJiKj3P1iuYgcF2Xx+81rUdaGu9zPoiUi8pmIJEXbfmCqVp9ze20+66OJVPN4v15QVXuUe+BcVGgt0BVIABYCvSIdV7kYNwAtyw17AXjQff4g8Lz7/GxgLM69ugcCM93hzYF17t9m7vNmIY77JOAoYEko4sa5yv1x7jRjgaFhasPjwL1+xu3lbj+JQBd3u4qtbBsDvgSGu89HADeHoA3tgKPc542BVW6sUbUuwvkIJC8AtwAj3OfDgS9Cub7KjXMK8EOI2r6Bcvmm3Pt+t48Q/P+3AgeHst0V7N9+94ty09U6n1aw7DOBOPf58/6WHcg6quGyH8dPXvOzXur052UItsW7gZF7tzvCkLODHP8B20og23hdeQAfAje4zxOAtGiKv1xb9uW1aGkD0B5YDyS7r78Erom2/cAeVa7nep3ba/pZH20Pqnm8Xx8e1nPDvwHAGlVdp6rFwOfA+RGOKRDn43zo4/69wGf4R+qYAaSJSDvgLGC8qu5S1d3AeGBIKANU1V858P7gQYnbfa+Jqk5XZ2/9yGdeoW5DRc4HPlfVIlVdD6zB2b78bmMiIsBpwCh3et//R9Co6hZVnec+zwWW4xywRNW6CLNA8oLv/28UcLq7TmulkvVVV1S0fQTT6cBaVd0Y5PmWUc0c5avW+dTfslX1J1UtdV/OADpUZ561WXaAovXzskZEpANwDvCO+zosOTsMAtnGI05EmuB8KXkXQFWLVTWLKInfD9+8Fk1tiAOSRSQOSAG2UD/2A7Nfvc7ttfisjyo1ON6Pelbc8K89sNnndTp164sEgAI/ichcEbnRHdZGVbeAszEDrd3hFbWnrrQzWHG3d5+XHx4ut7ldx9/z6d5V3Ta0ALJ8vsyEvA3inDrRD5hJ/VkXoRDI/rJvHHcdZuOs06Apt77KO05EForIWBE5PIiL9ZdvfIUjlwwHPqvgvVC1e6+K9gtf4fgfXIfTQ8afqtZRTfnLa77qyudIuLwC3A943ddhz9lBUJ3jh7qmK7ADeN89NegdEUkleuIvzzevRUUbVPV34B/AJpyiRjYwl+jbD0zlGlpuhyjZB2sqwOP9qGfFDf/8/dJa1+6ZO0hVjwKGAreKyEmVjFtRe+p6O6sbdyTb8x+gG9AX58P+n+7wOt0GEWkEfA3cqao5lY3qZ1idaUeYBNKmkLa7ivU1D+eUjSOBfwPfBGu5VJ1vQt3uBGAY8JWft0PZ7uoI9f/gEaAU+LSCUarzmRCoivJamdD8DIv2fd0vETkX2K6qc30H+xm1rrc/FNtKuMThdCX/j6r2A/bgdKmOOlXktTrLLXKej3Oq7UFAKs62VF5d3w9M5aIxt5kKVON4P+pZccO/dKCjz+sOQEaEYvFLVTPcv9uB0Tjdx7bt7Qru/t3ujl5Re+pKO4MVdzplu2yHrT2quk1VParqBd7GWR9UEau/4TtxuvTHlRsedCISj5PoPlXV/7qDo35dhFAg+8u+cdx12JSadfU/QAXrax9VzVHVPPf5GCBeRFoGY9kV5Btfoc4lQ4F5qrrNT2wha7ePivYLXyH7H4hzcdJzgcvd07wOEMA6qrZK8pqvuvI5Eg6DgGEisgGni/ZpOD05wpKzg6Waxw91TTqQrqp7e66Nwil2REv8vsrntWhpw2BgvaruUNUS4L/A8UTZfmCq1JBy+17Rsg9WSzWP96OeFTf8mw10d6/8nIDTbfC7CMe0j4ikikjjvc9xLji3BCfGvVfovxr41n3+HXCVOAYC2W4XpHHAmSLSzK3En+kOC7egxO2+lysiA93zoK/ymVdIlbu+wB9w1sfeNgwX5y4aXYDuOBfa9LuNuV9cJgEXu9P7/j+CGa/gnLO8XFVf8nkr6tdFCAWSF3z/fxcDEyv6Mlodlawv33Ha7r2+h4gMwMnvmUFYdkX5xldF20ewXEYFp6SEqt3lVLRf+ApJPhWRIcADwDBVza9gnEDWUU2WXVFe81WnPy+DSVUfUtUOqtoZp50TVfVywpCzg6UGxw91iqpuBTaLSE930OnAMqIk/nLK57VoacMmYKCIpLi5d+86iJr9wASkweR2H9GyDwasBsf70U/rwFVN6+ID5+r/q3CuFPxIpOMpF1tXnKsWLwSW7o0P59zfCcBq929zd7gAr7ttWQz095nXdTgXuVwDXBuG2D/D6d5cglMVvj6YcQP9cQ7U1gKvARKmNnzsxrgIJ2G08xn/ETeelfjcMaSibcxdv7Pctn0FJIagDSfgdC9cBCxwH2dH27oI98PfOgOewPniCZDkrrM17jrsGuL1dRNwkzvObW4+WIhz4cnjg7TsivKN77Ir3D6CsPwUnGJFU59hIWt3Bft3RftFf+CdqvaFWi57Dc55z3vX+9678RwEjKlsHQVh2X7zmu+yK9ov6vsDn7v0EIacHcS4q3X8UBcfOKdJzXG3y29w7k4UNfG7bfCX16KmDcDfgRU4n/Ef49wRLmr2A3sEvJ7rbW6vzmd9ND+o5vF+fXiI23BjjDHGGGOMMcaYqGSnpRhjjDHGGGOMMSaqWXHDGGOMMcYYY4wxUc2KG8YYY4wxxhhjjIlqVtwwxhhjjDHGGGNMVLPihjHGGGOMMcYYY6KaFTeMMcaEhYh0FpEl5Yb1F5FX3efXiMhrIY7hThFJCeUyjDHGGGNM+Flxw9R7IhIX6RiMMf6p6hxVvT2Mi7wTqFZxQ0RiQxSLMSbMRORqEVntPq6OdDzGmLpDRH4UkSwR+SHSsZiaseKGCSv3l9sVIvKhiCwSkVEikiIip4vIfBFZLCLviUiiiAwQkf+6050vIgUikiAiSSKyzh3ezU1Ec0XkNxE51B3+gYi8JCKTgOcj2GRjjB8i0tXd5+/zdxAhIgeLyAQ3T0wQkU7u8A9E5D8iMklE1onIyW7OWC4iH/hMf6aITBeReSLylYg0EpHbgYOASW5u8DueO3yDiDwqIlOAS8LxPzHGhJaINAceA44FBgCPiUizyEZljKlDXgSujHQQpuasuGEioSfwlqr2AXKAu4EPgD+q6hFAHHAzMA/o505zIrAEOAbnoGSmO/wt4K+qejRwL/CGz3J6AINV9Z6QtsYYUy0i0hP4GrgWmF3BaK8BH7l54lPgVZ/3mgGnAXcB3wMvA4cDR4hIXxFpCfwfzv5/FDAHuFtVXwUygFNV9dSKxvNZTqGqnqCqnwel4caYkBCR50XkFp/Xj4uIv8/+s4DxqrpLVXcD44Eh4YrTGBN+1cgPqOoEIDdswZmgs+KGiYTNqjrVff4JcDqwXlVXucM+BE5S1VJgjYgchvMLy0vASTiFjt/cX1iPB74SkQXAm0A7n+V8paqe0DfHGFMNrYBvgStUdUEl4x0HjHSffwyc4PPe96qqwGJgm6ouVlUvsBToDAwEegFT3dxwNXCwn2VUNd4X1WybMSYyPgf+6PP6UuArP+O1Bzb7vE53hxlj6q9A84OpB+xaBCYStBrj/gYMBUqAn3F6eMTi9NKIAbJUtW8F0+6pRYzGmNDIxvlyMQinGBEo37xR5P71+jzf+zoO8OD8OntZFfOUKsazHGJMFFDV+SLSWkQOwimg7lbVTX5GFX+ThzY6Y0wkVSM/mHrAem6YSOgkIse5zy/DKVp0FpFD3GFXAr+4z3/FuQDgdFXdAbQADgWWqmoOsF5ELgEQx5HhaoQxpkaKgQuAq0TkT5WMNw0Y7j6/HJhSjWXMAAbtzSnudX16uO/lAo0DGM8YE11GARfj/EJb0alk6UBHn9cdcE5VM8bUb4HkB1MPWHHDRMJy4GoRWQQ0xzlf/lqc00sW4/z6OsIddybQBqfIAbAIWOR2SQfnS8/1IrIQ51fg88PTBGNMTanqHuBcnGtmNK1gtNuBa908cSVwRzXmvwO4BvjMnX4GTlEUnOv0jBWRSVWMZ4yJLp/jFEQvxvki48844EwRaeZeSPRMd5gxpn4LJD+YekD2f0c0JvREpDPwg6r2jnAoxhhjjKlH3B9IdqrqqZWMcx3wsPvyaVV9PyzBGWMiKsD88BvOjxyNgEzgelW1AmgUseKGCSsrbhhjjDHGGGOMCTYrbhhjjDHGGGOMMSaq2d1SjDHGGGNMvSEiR+DcQtpXkaoeG4l4jDF1h+WH+s16bhhjjDHGGGOMMSaq2d1SjDHGGGOMMcYYE9WsuGGMMcYYY4wxxpioZsUNY4wxxhhjjDHGRDUrbhhjjDHGGGOMMSaqWXHDGGOMMcYYY4wxUc2KG8YYY4wxxhhjjIlqVtwwxhhjjDHGGGNMVLPihjHGGGOMMcYYY6KaFTeMMcYYY4wxxhgT1ay4YYwxxhhjjDHGmKgWF+kAIqVly5bauXPnSIdhTL01d+7cnaraKtJx1ITlB2NCK5rzA1iOMCbUojlHWH4wJrQqyw8NtrjRuXNn5syZE+kwjKm3RGRjpGOoKcsPxoRWNOcHsBxhTKhFc46w/GBMaFWWH+y0FGOMMcYYY4wxxkS1Ol/cEJEhIrJSRNaIyIMVjHOpiCwTkaUiMjLcMRpjjDHGGGOMMSZy6vRpKSISC7wOnAGkA7NF5DtVXeYzTnfgIWCQqu4WkdaRidYYY4wxxhhjjDGRUKeLG8AAYI2qrgMQkc+B84FlPuP8GXhdVXcDqOr2sEdpTDklJSWkp6dTWFgY6VBCLikpiQ4dOhAfHx/pUIyJGg0lR1h+MKb6Gkp+AMsRxtREQ8kRNckPdb240R7Y7PM6HTi23Dg9AERkKhALPK6qP/qbmYjcCNwI0KlTp6AHW594vRBT509aqrvS09Np3LgxnTt3RkQiHU7IqCqZmZmkp6fTpUuXkC5LRIYA/8LZz99R1efKvX8S8ArQBxiuqqN83rsa+D/35VOq+mFIgzWhUVwMHg8kJ0c6klprCJEAXTsAACAASURBVDkinPnBNCCqzt96ut9Aw8gPYDmi3vJ6nf2zHm+7kdYQckRN80Nd//rqb21puddxQHfgFOAy4B0RSfM3M1V9S1X7q2r/Vq2i8u5SYTF1KjRqBNu2RTqS6FVYWEiLFi3qbcLZS0Ro0aJFyCvHPqeoDQV6AZeJSK9yo20CrgFGlpu2OfAYTmF0APCYiDQLacCm1oqKYM0a93vM//4Hffs6RY1DDoGdOyMdXq01hBwRrvxgGpjx4yEtDXJzIx1JyDSE/ACWI+qtU06Bhx+OdBT1WkPIETXND3W9uJEOdPR53QHI8DPOt6paoqrrgZU4xQ5TQ8uXQ0EBbNoU6UiiW31OOL7C1M59p6ipajGw9xS1fVR1g6ouArzlpj0LGK+qu9zT18YDQ8IRtKm+wkL405+gaVPo3h0ObpzJXeeuYvruQ9F77oUdO+Dmm/f/ehvFGkKOaAhtNGG2di3k5MDu3ZGOJKQayr7TUNrZYKjC7Nmwbl2kI6n3GsK+U5M21vXixmygu4h0EZEEYDjwXblxvgFOBRCRljinqdgeVQvZ2c7fevyjSL2XlZXFG2+8Ue3pzj77bLKyskIQUa35O0WtfTCnFZEbRWSOiMzZsWNHjQM1NVdUBBdeCJ99Bn++toQ3ev6Lfnum8EbsXzl+0+cMnvs8y299DUaNgh9+iHS4Ua0e5gjTUBQXO389nsjGUY9ZfjA1tnOn8ytFSUmkIzEhVJdzRJ0ubqhqKXAbMA5YDnypqktF5AkRGeaONg7IFJFlwCTgPlXNjEzE9UNOjvM3Ly+ycZiaqyjpeKo4GBwzZgxpaX7P6oq0QE5Rq9W0dtpa5D36KIwdC2+9XsK/153Lzavu4tv3d7NjVxz/+hfMnw8nfvxntrc6HD75JNLhRrV6mCNMQ7H3S5MVN0LG8oOpsb3dvvcWIU29VJdzRF2/oCiqOgYYU27Yoz7PFbjbfZgg2NdzY9UWoF1EYzE18+CDD7J27Vr69u1LfHw8jRo1ol27dixYsIBly5ZxwQUXsHnzZgoLC7njjju48cYbAejcuTNz5swhLy+PoUOHcsIJJzBt2jTat2/Pt99+S3LkLuQYyClqlU17SrlpJwclKhM0K1fCyy/Dtdcof556Dfz0E7z7LlxzDU2A22+HwYOhXz/hzo7vMnLMYOfXoaSkSIcelephjjANhRU3Qs7yg6mxzW5HWStu1Gt1OUfU+eKGCTNVssfNBAaS948RcO/fIx1R9LvzTliwILjz7NsXXnmlwrefe+45lixZwoIFC5g8eTLnnHMOS5Ys2Xe14ffee4/mzZtTUFDAMcccw0UXXUSLFi3KzGP16tV89tlnvP3221x66aV8/fXXXHHFFcFtR+D2naIG/I5zitqfApx2HPCMz0VEzwQeCn6IpjbuvBNSUuDZ9q/B0yPh6afhuuvKjNOrFzzyCDz22LFcy0DOmDABzjknQhEHkeUIYwLX0Ioblh9MNLGeG+FnOaKMOn1aiomAxYvJWfE7ALl7bPOoLwYMGFDmNkqvvvoqRx55JAMHDmTz5s2sXr36gGm6dOlC3759ATj66KPZsGFDuMI9QCCnqInIMSKSDlwCvCkiS91pdwFP4hRIZgNPuMNMHbFgAfz4Izz8h+W0eeYOGD4cHvJff3rwQWjXTvl37F3wzTdhjrT+ivYcYRqQvcUNb/lrR5tQsfxgAmY9NxqkupQjrOeGKSs/n2yaApBXkhjhYOqJSiqf4ZKamrrv+eTJk/n555+ZPn06KSkpnHLKKX5vs5SYuH/9x8bGUlBQEJZYKxLAKWqzcU458Tfte8B7IQ3Q1Nh//gPJSV7+POosOOoo53SUCq6QnZAA11wjPP/cEH4f/RDt3/RCTJQXYi1HGBO4htZzw/KDiSbWcyP8LEeUEeVHhCboior2FTdyPXZuZLRq3LgxuRXc7iY7O5tmzZqRkpLCihUrmDFjRpijM2a/7Gz49FPlsrSxNEvMd3pjpKRUOs3114NXY/gg81xYsiRMkdYvDSVHiMgQEVkpImtE5EE/73cSkUkiMl9EFonI2ZGI01SD3S0l5OpbfgggD9wkIotFZIGITBGRXu7wziJS4A5fICIjwh99lNnbc8PullKv1eUcYT03TFmFhWS7FxHNLa38C4apu1q0aMGgQYPo3bs3ycnJtGnTZt97Q4YMYcSIEfTp04eePXsycODACEZqGrpPP4U9e4Rb9jwK/3kKOvjtfFNGt25w2qBC3p16PQ//NBrp0ycMkdYvDSFHiEgs8DpwBs6FhWeLyHequsxntP/DOc3tP+4XmjFA57AHawLX0HpuREB9yg8B5oGRqjrCHX8Y8BIwxH1vrar2DWfMUc16bjQIdTlHWHHDlFVURA5NAMgjFUpLIc42k2g0cuRIv8MTExMZO3as3/f2nu/WsmVLlvj8In7vvfcGPT5jAEaNUg5PWM3RhxTCDTcEPN2VNyRx7dSuzP56EwNs86yRBpAjBgBrVHUdgIh8DpwP+H6pUXA/9KApgd+FyUSKFTfCoh7lhyrzgKrm+IyfSuC3mje+Skogw02hVtyo9+pqjrDTUkxZvqel0BiKiiIckDGmvtq9G379BYYVj3Jug1KNQur550N8TCmj5nS2PGUq0h7Y7PM63R3m63HgCvdixGOAv1Y0MxG5UUTmiMicHTt2BDtWE6j/Z+/Ow+S6y0PPf9/aqxf1Lqm1WLtsZMmWbUmsxjbY2E4GmwRIDOEOTHzj8NyYkJgwgcmNE2AyQ2Ae4CbXyYUEckkYMODcy4ggYyB4AeNFsi1LlmRbiyWrtfa+1l7v/HGqqqu7q7qrW911qqvez/Po6apT55x62+46dc573t/7s4aiZnZKOQ4gIn8gIseBLwB/mPfSusywtcdF5Ppib2LHB5zEhip4vZbcMK6x5IaZID4SJ4rTa2OEBrtoMMYsmB//GFJp4Y7wz+A975nVti0tcPM1vXw/+R70V08tUIRmkSvUlXbyHdkPAP9dVVcBvwb8i4gUPDdS1a+p6g5V3dHR0THPoZqSWeWGmZ1SjgOo6gOqugH4U5zhagDngMtU9RrgPuDbIrJk8raZ7e34kB2SctllltwwrrHkhplgaGD8TohVbhhjFtLu/5liqVxk13tXz9hEtJD3/+4STrKO5795cAGiM1WgC1id93wVU4ed3A18D0BVnwJCQHtZojNzYw1FzeyUchzI9yDwHgBVjalqb+bxc8BxYPMCxbn4ZZuJbtxoyQ3jGktumAkGB8aT2Va5YYxZKIkEPPyjFP+L/hDPf/idOe3jzrvC+CTJQz+ymZ1MQXuBTSKyTkQCwF3A7knrvA68E0BE3oCT3KjRmvJFwio3zOzMeBwQkU15T38dOJpZ3pFpSIqIrAc2ASfKEvVi1Nfn/OzstNlSjGssuWEmGBxyqvfa6qNWuWGMWTD79sHgWIDbG34B73jHnPbR2grXrz/LD3veBKdOzXOEZrFT1SRwL/AIcARnVpRDIvLZzIwIAJ8Afk9EXgS+A3xEVa2ZYCWblNz43Odg/34X4zEVrcTjwL0ickhE9uMMP/lwZvnbgQOZ48NDwEdVta/Mv8Likb1maGy0yg3jGpsGw0wwlOkXvao9wonRRoj1uhuQMaYqPfrzNODhxlsClzQj07vfF+S+v97Ka9/8F9bd/x/mL0BTFVR1D06j0Pxl9+c9Pgy8tdxxmUuQl9xIp+H++51rqu02WacpooTjwMeLbPevwL8ubHRVJD+5kUg4zUWlUMsTYxaOVW6YCQZHvACs7IgzQgMatcqNxWhgYIC/+7u/m9O2X/nKVxgbG5vniIyZ6LF/G2EbB2i/89KuK99991IAfvjd0fkIq2bYMcIsWnmzpWRHpliR6fyy44OZk2y1Rn2989OGplStSj5GWHLDTDA47PxJrOxIoHgYG7QD02JUyQcdY+JxePK5EDfxKNx66yXta+Mm4YqW8/zw5U1WBjsLdowwi1ZeQ9FsciMadS+camTHBzMnsRgEAhAMOs/tO7lqVfIxwoalmAmGxpw/iVXLkwCMDKaodzMgMyef+tSnOH78ONu3b+eWW25h6dKlfO973yMWi/Ebv/EbfOYzn2F0dJTf+q3foquri1QqxZ//+Z9z4cIFzp49y0033UR7ezuPPvqo27+KqULPPgtjiQA3rnsdli+/5P29+4YhvvKD6xl+dB+Nt75lHiKsfnaMMItW3rCUpHOqYpUb88yOD2ZOYjEnsREIOM+tcqNqVfIxwpIbZoLBUedPYsVyZ0rY4YEUy9wMqAr80R/Nf7Oz7dvhK18p/vrnP/95XnrpJfbv389PfvITHnroIZ599llUlTvuuIMnnniC7u5uVqxYwY9+9CMABgcHaWpq4ktf+hKPPvoo7e02G6JZGI/9OIoQ4IY7m+dlfzd/aDlf/EGAp799glsWYXLDjhHGzEJecqMWhqXY8cEsGpOTG1a5URZ2jJjIhqWYCQYjAYLEaGt3GgAND6Zdjshcqp/85Cf85Cc/4ZprruHaa6/l5Zdf5ujRo2zbto2f/exn/Omf/im/+MUvaGpqcjtUUyMe3T3EVRyg9TdvnJf9vemWJXhI8cvHk/Oyv1pjxwizqFjlRlnZ8cGULDssxZIbNaXSjhFWuWEmGIwEaPIO09jk5L1Ghm1GvEs1XeazHFSVT3/60/z+7//+lNeee+459uzZw6c//Wne9a53cf/99xfYgzHzJxaDXx1u5qOB78Ob7pmXfS5ZAtuXnuMXr6+BSATC4XnZb7nYMcKYWaixhqJ2fDCLRixGLNCIRwL4wZIbZWLHiIkqvnJDRG4TkVdE5JiIfKrA6x8RkW4R2Z/59x/diLNaDMWCNHlHaWh28l7Dwy4HZOaksbGR4cz/vFtvvZVvfOMbjIyMAHDmzBkuXrzI2bNnqaur40Mf+hB/8id/wvPPPz9lW2Pm27PPKNFUgBuvHQa/f972+7Y3JXha30jiiafmbZ/VzI4RZtGyyo0FZ8cHMyexGO/t/m/8/j9nZkGz5EbVquRjREVXboiIF3gAuAXoAvaKyO7MvPT5vquq95Y9wCo0GAuxxDdGY0srAJm/U7PItLW18da3vpWtW7dy++2388EPfpA3v/nNADQ0NPCtb32LY8eO8clPfhKPx4Pf7+fv//7vAbjnnnu4/fbb6ezstGZgZt499v1uhHauv2vlvO73be/p4G921/HCv51h16VNwFIT7BhhFq0Cs6VYcmN+2fHBzEksxpHEBi6eyUxFYMmNqlXJx4iKTm4Au4BjqnoCQEQeBO4EJic3zDwZjIdp8o/R2Oq0ER225Mai9e1vf3vC849//OMTnm/YsIFbC0zD+bGPfYyPfexjCxqbqV2PPhzlal6ct34bWW+7rQGAXzztZ9e87rl62THCLEpWuVEWdnwwsxaL0ZNqITmYGRhgs6VUtUo9RlT6sJSVwOm8512ZZZO9V0QOiMhDIrK62M5E5B4R2Sci+7q7u+c71qowlAzTFIjQ0OKUi4+MissRGWOqRTQKT722jJvaDsDqoofqOenshLWhczxzwjr0G1PVCsyWEo26F44xxpGIJBlKNXBhIIiCVW4YV1R6cqPQlfXkDpc/BNaq6lXAz4BvFtuZqn5NVXeo6o6Ojo55DLN6jCaD1AcSNLQFARge9bockTGmWjz72BjRdJAb35ZakP1f1dnNoYGVoNYI2ZiqlddQ1Co3jKkcfaPOtUMs4WWIJZbcMK6o9ORGF5B/e28VcDZ/BVXtVdXs19o/ANeVKbaqFEv7CfpTBOr9BIgxPGbJDWPM/HjsX04jpLn+IxsWZP9Xbk7yanoj8RNdC7J/Y0wFKFC5YckNY9zXE6nPPb7AMktuGFdUenJjL7BJRNaJSAC4C9idv4KIdOY9vQM4Usb4qo6T3FAQoZFhRiKW3JgrrZG7x7Xye5pL9+hjwnY5QMvtb1qQ/W/dGSaJn1d/9vqC7H++1cJnpxZ+R1NmeQ1Fq7lyo1Y+O+X6PUuYffGjInIwM/PiL0VkS95rn85s94qIWMvqInojdbnHltxYeLVwjJjL71jRyQ1VTQL3Ao/gJC2+p6qHROSzInJHZrU/FJFDIvIi8IfAR9yJtjrENEAw4PwhNcgow9FK7zlbmUKhEL29vVV/4FFVent7CYVCbodiKlw0Ck+dW8ONa09CMLgg73HlO5xGyC89ObAg+59PtXCMsOODmXeq5DIaVVy5UQvHByjfMSJv9sXbgS3AB/KTFxnfVtVtqrod+ALwpcy2W3Burl4J3Ab8XWZ/ZpLeqFVulEstHCPmenyo+CtXVd0D7Jm07P68x58GPl3uuKrVhOSGZ4yRqN/liBanVatW0dXVRS00rg2FQqxatWrB30dEbgP+C+AF/lFVPz/p9SDwzzhD03qB31bVkyLiB/4RuBbnmPfPqvp/L3jAZoJnfnCOmHZy080Ld054+Ztb8ZLk0MHK/7KvlWNEuY4PpkZkExtQ1ZUbtXJ8gLIdI2acfVFVh/LWr2e8x9+dwIOZIfCvicixzP6eWuigF5ueWEPu8QWW2WwpC6hWjhFzOT5UfHLDlE86DQkCuZuqIU+cWKKii3sqlt/vZ926dW6HUTXy7rrcgtOLZ6+I7FbV/Gmh7wb6VXWjiNwF/DXw28D7gaCqbhOROuCwiHxHVU+W97eobY995xzCMq7/3zYu2HuEQrAxfIaXXm9csPeYL3aMMGYO8i+WUilS3X1AK7GYUrgH/eJkx4d5V2j2xTdOXklE/gC4DwgA78jb9ulJ2xaaubHm9cbHv3utcmNh2TGiOLtyNTnZY1A2uRH0JIkl7U/EVITcXRdVjQPZuy757mR8tqSHgHeKiODcfakXER8QBuLAEKasHns6xHbfSzS/6YoFfZ+ty3s4NGiVAsZUpfzkRjpN8qWXAYhFbZIkM61SZl9EVR9Q1Q3AnwL/eTbbisg9IrJPRPZV+930YnoTTYS8cZa2pyy5YVxjV64mJxZJA3nJDW+CWMKGFZqKUOiuy+Q7J7l1Mv16BoE2nETHKHAOeB34f1S1b/Ib2InJwolGlKcurufGDV0gC3t39cp1EY6l1hHpGV3Q9zHGuGBy5UbCOW9Jq0wYsWLMJDPOvjjJg8B7ZrOtqn5NVXeo6o6Ojo5LDHdx6kk20143xrKOtCU3jGssuWFyYkPOoNVg2Ln4CHhTxFKW3DAVoZQ7J8XW2QWkgBXAOuATIrJ+yop2YrJgnn3oFDFC3HDzwo+EvHIrKB5eefz8gr+XMabM8i+WUimSifGvgWrru2HmVSmzL27Ke/rrwNHM493AXSISFJF1wCbg2TLEvOj0ppppC0dYttSGpRj3WHLD5MSGnYNQMORcIwa9KWJJa8tiKkIpd05y62SGoDQBfcAHgR+rakJVLwJPAjsWPGKT8/h3zyOkuf53Ny/4e228dgkAx/f1L/h7GWPKrEjlBkAsauNSTGElzr54b2b2xf04fTc+nNn2EPA9nOajPwb+QFVTZf8lKl0ySS+ttNVHWbbckhvGPZbcMDm55EbY+bMI+lLE01a5YSrCjHddMs8/nHn8PuDn6syR9TrwDnHUA28CXi5T3AZ4/NkwV/lfpvXatQv+XhveshyA44ftNq4x1eaXT3n5Gz7mlO1Nrtw4drrodsao6h5V3ayqG1T1rzLL7lfV3ZnHH1fVK1V1u6relElqZLf9q8x2l6vqw279DhUtFqOHdtobYyxb7rHZUoxrLLlhcmIjzkEoGMokN/wpYimr3DDuK/Guy9eBtsw0bfcBn8osfwBoAF7CSZL8k6oeKOsvUMPikRS/6t7EDZumG948f5o2dtBGD8dfs683Y6rNP363gY/zN/wxX0ZT6YmVG8+95GJkxtS4WIxe2mhrjLOs08MY9YyMuB2UqUV25WpyplZupIml/W6GZEyOqu4B9kxadn/e4yjOtK+TtxsptNyUx77vHCXCFdxwS6A8byjChtBZjp+vK8/7GWPKJhl3KjX+C3/E2458C8/KvMqNF18Gfs2lyIypbalInH46aFtyimXLneHtF/oDNLgcl6k9dmvL5ERHnFbjwXon5xX0W3LDGHNpHv/+RQDe/h8Xvt9G1oaWPo4Ptpft/Ywx5ZFMKB04x5QzQw2kknnJjZeOFtvMGLPABroTpPHS3pxk2TJn2fmBkLtBmZpkyQ2TkxuWUuf02bDkhjHmUj22t54rg0dp37q8bO+5YUWE1+PLrZeZMVUmlVSCOP10EkmZ2HNjyPrsGOOW3gvODdK25hRLnL7ejIzZZaYpP/urMzmxMaf5c7ZyIxCAmJaplNwYU3USo3Ge7L2cGzadK+v7btgAabycOjhU1vc1xiysVFIJEQUgmZQJlRvRhDVAN8Ytvd1O/5u2ljShTMFGNCYuRmRqlSU3TM54ciNTuRFQEgRIp6fbyhhjCnv+W4cZpYEbbi1vaeqGrWEAjj91sazva4xZWPnJjUTKQzI5/losYae0xril56JzsdDeNp7ciMTsM2nKz/7qTE5sNNtzwxmKEgw4d0RsJidjzFw8/q89ALz97k1lfd8Nu9oAOH7AWrUbU01SqfFhKcmUkErmzZaStMoNY9zS1+dcM7S2CmHn/oJVbhhXWHLD5MQizklCsCGT3AhmltswVmPMHDz+XD1XBF9j+Rtayvq+nTtWEmaM46+kyvq+xpiFlUyAlxQ+EiRTQjLv5otVbhjjnv5+52dLm8eGpRhX2TeByckNS2l0+mwEMwenWFSLbWKMMQWlhsf4Zd8WbrjiQtnfW1pbWO85yfHT1hC51onIbSLyiogcE5FPFVnnt0TksIgcEpFvlztGU7pUSjPJjSSJlJDKy1/GUj73AjOmxvUPOImMplZvrnIjErdqKlN+ltwwOdkkxnjlhnOgys6iYowxpdr/zwcYookbbguX/81FWNfQzcmexvK/t6kYIuIFHgBuB7YAHxCRLZPW2QR8Gnirql4J/FHZAzUlSyWdyg1/rnIjb7YUG5ZijGsGBj00MYC3LjheuRG3y0xTfvZXZ3Jyw1KWOONRgqFMcmPY5lM0xsxOtt/GDXdvdOX917aNcGq0zZX3NhVjF3BMVU+oahx4ELhz0jq/Bzygqv0AqmpdaCtYKgU+kpnKDQ+pVF5ywyo3jHFN/5CXFvohGMwNa49awtG4wJIbJidXuZEZlhIIOX8eVrlhjJmtx19oZGPoNCs21bvy/mtWJRlML2Gg34bV1bCVwOm8512ZZfk2A5tF5EkReVpEbiu2MxG5R0T2ici+7u7uBQjXzCSVIjcsJZnykEyOj+m35IYx7ukf9tLMAASDiEDIEyMSt8+kKb+KT26UMl42s977RERFZEc546smueRGk1NPFgxbcsMYM3vpvgF+MbCNG97g3k3wtRud4XWn9ve7FoNxXaFudpOzXT5gE3Aj8AHgH0WkudDOVPVrqrpDVXd0dHTMa6CmNMlMcsMvUys3opbcMMY1/SP+XOUGQMgTJ5q0z6Qpv4pObpQyXjazXiPwh8Az5Y2wusRi4COBJ5RpKJpJbsRHLblhjCndwX9+gX5aueH2OtdiWLO1AYCT+3pci8G4rgtYnfd8FXC2wDr/n6omVPU14BWcZIepQLnKDUmRTHtIJsdfi6WsgbApbKYbpSJyX6ap8AER+XcRWZP3WkpE9mf+7S5v5ItH/0hgQnIj7I0TseSGcUFFJzcobbwswOeALwDRcgZXbWIxnPnjxbnZFaxzxsrFLLlhjJmFn/+PAQBu+Mh612JYe107AKcOjbgWg3HdXmCTiKwTkQBwFzD54uQHwE0AItKOM0zlRFmjNCXLJjf8nhTJtJBKOecrHkkTS1tyw0xV4o3SF4AdqnoV8BDONUVWRFW3Z/7dUZagF6H+sYnJjZA3YZUbxhWVntyYcbysiFwDrFbVf5tpZzZednqxuBCU8eah48mNZLFNjDFmih8/v5Q31J/isk1B12Jov/Yywoxx8pgdv2qVqiaBe4FHgCPA91T1kIh8VkSyFymPAL0ichh4FPikqva6E7GZSSolTkNRSZNIeUkmQUgT8iWt54YpZsYbpar6qKqOZZ4+jVPlZWahfyw4MbnhSxJNWsLRlF+lJzemHS8rIh7gy8AnStmZjZedXvHkRqrYJsYYM8HYaxd4fPQ6btt+wdU4pLGBtd7TnDxjJ1e1TFX3qOpmVd2gqn+VWXa/qu7OPFZVvU9Vt6jqNlV90N2IzXRS6fzKDU+ukiPoTREjAOm02yGaylNKY+F8dwMP5z0PZW6MPi0i7ym2US3fQI1GIZrM9NzwO9+5YV+CSDrgcmSmFlV6cmOm8bKNwFbgMRE5CbwJ2G1NRecmlpiU3Kh37oLExiy5YYwpzeP/7QgxQtz2201uh8Ka+l5O9Ta4HYYxZp4kU+L03PCkSaQ9JFOCjxRBX4oYQSY04TDGUUpjYWdFkQ8BO4Av5i2+TFV3AB8EviIiGwptW8s3UAeckai0eIbA41xahnxJotYHx7ig0pMb046XVdVBVW1X1bWquhanlOwOVd3nTriLWyzuIegZ768RqLPkhjFmdn78b0nCjPH2393odiisbR/h5Gi722EYY+ZJKi14RfHlV26IJTfMtEppLIyI3Az8Gc51RCy7XFXPZn6eAB4DrlnIYBej/sykZC3+8R5XYX+KSNq9oammdlV0cqPE8bJmnsSSHoLe8RODXOVGxJIbxpjS/PjV9dy47Aiheq/bobBmVYq+dAvDg1aqbkw1SKUFnyedG5aSTAk+SRHyW3LDFDVjY+FM/76v4iQ2LuYtbxGRYOZxO/BW4HDZIl8kssmN5sBYblnInyRqw1KMCyq++5Kq7gH2TFp2f5F1byxHTNUqlvQS9OQlNxqccrJ4xC4MjDEzO/HY67yaXM8fvO0Jt0MBYO1GHzwBp/Z1s/Wdy9wOxxhziVJpwetRp6Fo2pvrwRH0p4gSgoTN7mYmUtWkiGRvlHqBb2RvlAL7Mv13vgg0AN8XZ8bA1zMzo7wB+KqIpHFuJ/h9gQAAIABJREFUCH9eVS25MUmuciOYn9xIW3LDuKLikxumfGIJL0FfocoNS24YY2b24384DVzGbXdP16utfNZsbQTg1HM9ltwwpgpkkxt+T4p42per3Aj601a5YYqa6Uapqt5cZLtfAdsWNrrFL5fcCEVyy8KBFBG1nlem/Cp6WIopr1jKO3FYSqOTcY1FC/ZdMsaYCX78WIh13lNsunW926EAsHaH02/j5KFRlyMxxsyHZNqD1+v03EikvaRSglfSBP3qJDescsOYsiuU3AgF0k41ldo1hCkvS26YnFjKR9A3XqVhyQ1jTKliUeXn567gtvVHEU+h5vTlt+y6VQSIceq43c01pho4lRs4PTfUQzJtlRvGuC3Xc6NufMbFcChNhLAlHE3ZWXLD5MRSfoK+8eahvoYQQppYzJIbxpjpPfmt1xjVem67ze1IxnnqQqzxdnHyjE1HZ0w1SKkHn1fxedIk8ys3AmrJDWNcMjAADd4x/KHxRuKhgDqVG/H4NFsaM/8suWFyYmkfQf945YaEQwSIE4u6GJQxGSJym4i8IiLHRORTBV4Pish3M68/IyJr8167SkSeEpFDInJQRELljL0WPPKdPvzEuemjl7sdygRrGvo41Wvjfo2pBqncsBQloV6SaQ8+T5pgIG3DUoxxSX8/tPiGITg+9WsoqEQJozFLbpjysuSGyYml/ROSG4RCBIkRixXfxphyEBEv8ABwO7AF+ICIbJm02t1Av6puBL4M/HVmWx/wLeCjqnolcCNgZ8Dz7GfPNfPm0H4at6x2O5QJ1raPcHK0w+0wjDHzIKWZYSneFEnNzJYiaYIBrHLDGJf090Ozd2JyIxxyqr5jw5bcMOVlyQ2TE0sHCAbyhqB4PASJEY/bsBTjul3AMVU9oapx4EHgzknr3Al8M/P4IeCd4szp9i7ggKq+CKCqvaqawsybnnMJXhhcz81bz7sdyhRrVqW4mG4nMmwXPcYsdkn14vWCz5MmkfY5lRv5w1KscsOYsuvvhxbP4MTKjUx9bHTEvntNeVlyw+TEdFJyAwhKnFisMpoDmpq2Ejid97wrs6zgOqqaBAaBNmAzoCLyiIg8LyL/e6E3EJF7RGSfiOzr7u6e91+gmj36j8dQPNz8G41uhzLF2o3OlNannqm8xIsxZnZS6sHrBb8nnancELyeNMFgZny/VW4YU3b9/dAigxAI5JaF65yf83Jj4YUX4KWXLn0/piZYcsPkxAgQDE5KbngSxOKW3DCuK/RHOLmkqNg6PuBtwO9kfv6GiLxzyoqqX1PVHaq6o6PDhjHMxs9+MEojQ+z8ve1uhzLFmq1OwuXUcz0uR2KMuVQp9eDzgc+bJqFeUpmeG6GQDUsxpuwOHoRolN5eaJW+SZUbzinZJVduXLwI73wn/Kf/dGn7MTXDkhsGAE07JZ3BwMTlQU+CWMKSG8Z1XUB+M4dVwNli62T6bDQBfZnlj6tqj6qOAXuAaxc84hrys0Od3NS8H19Hi9uhTLH2ujYATh4eczkSY8ylUAUlU7nhTZNUH8m0TOy5YcNSjCmPsTHYsYPRL3+Ns2dhg7w2MblRl0luDF/iZ/ITn3BKQ44evbT9mJphyQ0DQHIsjuIhGJqYyHCSG/ZnYly3F9gkIutEJADcBeyetM5u4MOZx+8Dfq6qCjwCXCUidZmkxw3A4TLFXfW6Dg1yIraSd+wadjuUglZc14mPBKdeS8+8sjGmYqUynZK8PvB5lCRep5LDowRDQowgmrDKDWPKor8f4nFefaYfgMt5ZWJD0Trn2iEycgktzvbtg299C5Yvh/PnYXT0kkI2tcGuWg0AsSFnSpS84xIAAW+KWMJbYAtjyifTQ+NenETFEeB7qnpIRD4rIndkVvs60CYix4D7gE9ltu0HvoSTINkPPK+qPyr371Ctnv7vLwPwlt/sdDmSwrx1QVZ7z3LyjN/tUIwxlyA74sTrFXxeJaFOQ1Gn54ZT1ZGMWnLDmLIYGgLglVedm6KXp49MqtxwLjGjo5eY3ACnegPgtdfmvi9TM3xuB2AqQy65EZ6Y7wp6k8SS9mdi3Keqe3CGlOQvuz/vcRR4f5Ftv4UzHayZZ0//dJggUa7+na1uh1LUmroeTvZWXrNTY0zp8is3/JJtKOr03MhWnUbH0lga05gyyCQ3Xj7TiIiyaexFaLwx93K4wbkxeknJjZMnSfjCJHdcTxjg+HHYWrnnGqYyWOWGAcbnoZ4yLMWXIp60yg1jTGHPvNLEtS2vEWgIzLyyS9a2DnJqtM3tMIwxlyCb3PD5MpUb+EmqB68H6uqdc5fImE1db0xZZCs3hpaztjNGKDU6IfGQrdyIjF7CkNBTp/jd0Ld588euI4UHTpy4pJBNbbDkhgHykhvhqcmNWMqSG8aYqRKvvsa+6FbedHXU7VCmtaYzwblkB/GYXfgYs1iNV24Ifm+aNF4SaR8+b5q6zLSTY6P2GTdTichtIvKKiBwTkU8VeP0+ETksIgdE5N9FZE3eax8WkaOZfx+evG3NylZucAWXh045y66+OvdyqMGp+o6OXUJy4+RJ/j3+Nl58ycf3wx92KjeMmYElNwwAsRGnm3EwPDGREfSniaWsyNMYM9WBb75AlDBvendlT527Zq2geDh9cMDtUIwxc5TruZGp3ACIqR+vR6lrcE5nx2xSJDOJiHiBB4DbgS3AB0Rky6TVXgB2qOpVwEPAFzLbtgJ/AbwR2AX8hYhU3rRgbhgaIo3wKpu5ov8pCIVg8+bcy+ElzrVD5BKSG2eORzkXbwfgs/qfSR2znhtmZpbcMADERp2zhmBdgeRG2npuGGOmenpPHwBv/M2VLkcyvTVXhAE49Xyvy5EYY+Yqv3LDlzktiaaDTnLDhqWY4nYBx1T1hKrGgQeBO/NXUNVHM1PFAzyNM908wK3AT1W1L9Oc/KfAbWWKu7INDXGGlYxRz+X9TztDUnzj1wvjlRtz/ExGIjzbvRaAP/5jOBJdzyMHV1xq1KYGWHLDAHmVG1OSG0osbZUbxphJVNl7pIFloUEuWyMzr++iNVc3A3DypcqcrtYYM7PJw1IAohrA582r3IhU9rHIuGIlcDrveVdmWTF3Aw/PdlsRuUdE9onIvu7u7ksId5EYGuJlrgAy08DmDUmB8cqNaHSOyY3XX2cvO/F503zyk86iV843jR8IjCmi4pMbJYyT+6iIHBSR/SLyywKlZqYExSo3AgEllq7cRoHGGJe8/DIHYpu5esMwUuHXE6uvW4qQ5tTRhNuhGGPmKNdQ1D8+LCWqTuVGuN6SG6aoQn8UBa+4ReRDwA7gi7PdVlW/pqo7VHVHR0dlD9WcF0NDvOp3GogWSm6EGrPDUua4/5MneZZdXLVhlOXLIexPcia9HM6cuZSoTQ2o6ORGiePkvq2q21R1O84YuS+VOcyqkE1uZMvIsoIBZ0yrMcbkSz72Sw6zhW1vrHM7lBkFVnawgrOcOl3RX3nGmGnkKjf8gt83ntzweaGu0bkxMxYBnngCvv51l6I0FagLWJ33fBVwdvJKInIz8GfAHaoam822NWloiNP+9fglwXLOT01uLHFujEZjhTaeWfq1U+xjBzt3CiKwoiPOGVbajClmRpV+plfKOLmhvKf1FMmomunFxpyzhmD9pORGEOJY5YYxZqLjD79KjBDbrl8EvdU8HtaELnCqO+x2JMaYORpvKOrJDe2PEcTr1bzkhge+9jX4y790J0hTifYCm0RknYgEgLuA3fkriMg1wFdxEhsX8156BHiXiLRkGom+K7PMDA1xxnsZK5aM4BHgqqsmvOyrC+AjQWSO1VRHXxhhkGZ23ejcQFnZmXaSG7Uw5MdckkpPbpQ01k1E/kBEjuNUbvxhmWKrKrnkRuPEREYw6Jw8aNLGuBljxh18agSArdsWRxn4mqYBTg02ux2GMWaOUgmnz4bXN165ESPk9NzIJjeiAtEoxOOuxWkqi6omgXtxkhJHgO+p6iER+ayI3JFZ7YtAA/D9zDD33Zlt+4DP4SRI9gKfzSwzQ0N06UpWXV4PDz8MzZO+X4NBQkSJxuZ2jvDqy87nfetVzqXqylXCWVbAgM16ZqZX6dNglDTWTVUfAB4QkQ8C/xkoOA+1iNwD3ANw2WWXzWOYi18s4hxEgg0Th6AEQ84UisnRMfxNlV9+bowpg9OnOdizHI+k2bKl0nPkjjUdEb5/YSmpFHi9M69vjKksqXgK8OD1e/Clx08FvXnDUiJRDxCz5IaZQFX3AHsmLbs/7/HN02z7DeAbCxfdIjU8zJnUMq5ZG4Bbb536utdLmMickxunM7e2V2cGBa1c4+cMK9H+gYIXh8ZkVfpZ6WzHuj0IvKfYizXX7GcWcsmNyZUbIecQEhuIlD0mY0yF+tWvOMg2Nq6OE14kIz3WXJYmiZ+zp6ypqDGLUSrmfHZ9gfGGooDTc6PJuTEzFvU4lRuxOQ70N8aURAeH6IovZdWq4uuEJEYkPrdLzdM9YfyeJMuWOc9XrPETJUz/eftsm+lVenKjlHFym/Ke/jpwtIzxVY3s3PDZqZuygmHnTyQ2bHdBjDEZzz7LQbmKbdctnn48azc5sZ56vtflSIwxc5GKOcNjvX4P/rxTFa9XCdZ5EdKMxbw2LMWYMhgYgEgqyMppJtUNSYzoXJIbqpwebWVl4xCezOYrVzk3W8+ctboNM72KTm6UOE7uXhE5JCL7gfsoMiTFTC+SKcwINwcnLM8lN4YsU2qMcYw+dYDjup5tV1f0V8gEa7Y2AnDqwKDLkZhymmk6+bz13iciKiI7yhmfKV0ym9zIaygK4POBeIQwEcbiXqdqI5Uan17FGDPvugad79TpKjfCnhiR+BzGgQ4Pczq9gtWt4/PIZpMoZy/YuFIzvUrvuVHKOLmPlz2oKlQsuREIOwcRq9wwxgCQTHL4+SiKh23b3A6mdJdd0wbAqVdsiF2tyJtO/hacYa57RWS3qh6etF4jTjPyZ8ofpSlVKuZMl+L1e/BMqNxw7uTWMeZUbqSizgvxOItm3Jwxi4kqZ0aaAGao3IgTTczhUvPiRU6zmjcvHx9Gmn2fMz3BIhsZ41g8t93MgopEBQ8p/MGJfxLBOie5ER+x5IYxBjh8mIMxZzTgYkpu1F++ina6OXXSZguvITNOJ5/xOZzZ1qLlDM7MjtNQFHyBSZUbmf4bdRJhLO5zhqWADU0xZqHEYnSllgMzVG5455bcSJ+/SBerWL16/Pu6s9P5eWawftb7M7XFkhsGgEhMCBFFJg1lyyY3rHLDGAM4/TbYRjiUZv16t4OZhYYG1njPcOrc4ukTYi7ZjNPJi8g1wGpV/beZdiYi94jIPhHZ193dPb+Rmhllkxtevyc3FSw4U8MC1EnUkhvGlMPQEGcyh9Js0qGQkDdOZA7Jje5jgyQIsHrdeIlWMAjtgSHODDfNen+mtlhywwDO9GlhmXrTKtzoHJQiw8lyh2SMqUR79/KSbztbrpRFN6XqmoZeTvbZiVENmXY6eRHxAF8GPlHKzmzGNXflhqUEvPj84/9rs1UcdZ6IcyGVnSnFZkwxZmFkkhtLl0QITHO/IOxLEEn6i69QxOlXnV4bqy+vm7B8ZcMgZyMts96fqS2W3DAAROJewp6pJwJ1S5yzhrFha8xljAH27eOg52q2bVt8HcvXtg3z+lgbaiNTasVM08k3AluBx0TkJPAmYLc1Fa1MybgzZb130rCUbJK1zhNlLOG3yg1jFtrQEF2sYlXH9AnEOl+CseTsqyVPv+Zcc6y+csmE5SubRjgTb5/1/kxtseSGASAS9xRMbtQ3OWcQo8PpcodkjKk0ySTdL13gQrx1UfXbyFqzIkFEw9iIgpox7XTyqjqoqu2qulZV1wJPA3eo6j53wjXTGR+W4sUfyKvcyNwYtuSGMWWSqdxYuWz6qu46f5yx1BySG2ecy9PV6ydWfaxojXJWO8c/48YUYMkNA0Ak7iPsmXoiYMkNY0zO0aMcjG8GFlcz0aw1651bvKcOjbgciSmHEqeTN4vEhIai+bOlZHpuhD1xJ7mRTWrYsBRjFka2cmPF9GWQdf4kkbkkNy4GCEmUtraJy9vblD5a0QGb0t0UV/FTwZryiCR8hL1Tkxt1Lc6US2MjltwwpuYdOMBBnKzGokxuvMEZv3tqfz87b2pwORpTDjNNJz9p+Y3liMnMTSqRHZbixV+o54Y35iQ3sqxyw5gFkewfpo82lnb2TbtenT/BWDo06/2f7mtgdbAbkdUTlre2CXGCjJ4dpGH5slnv19QGq9wwAESSPsK+ApUbLU7GdXS03BEZYyrOgQMclKtpb1eWLcLzijXbnUZkVrlhzOKTG5YS8E7sueFzTmWd5EbeXWJLbhizIAYvOMNCWpZO3yy0LpgirgGSs5yT4PRIC6sb+6csb+1wqi/7TttFiSnOkhsGgEjST9iXmLK8vtWp3LDkhjGGAwc4ENrJtm0yZdroxaD5qstYwiAnj0491hljKtt4Q9EiPTd8cSIpPwp0027DUkyOiNwmIq+IyDER+VSB198uIs+LSFJE3jfptZSI7M/82z1521o00O18h7Z0BqddLxxwEpKRyOz2fzbWyormqRu1dTrJy97TY7PboakpltwwAERSAcL+qanVUFNmWEpkEV7JmKpSwslJUES+m3n9GRFZO+n1y0RkRET+pFwxV5vki4c4GL+ca65xO5K5kc7lrJHTnOqyrz5jFpvcsJSgb8JUsF6v87jOF2csHeKHvJtVdNHTbdMiGRARL/AAcDuwBfiAiGyZtNrrwEeAbxfYRURVt2f+Wa8eYKDHuV5obp+5cgNgbBa5CE0kOZdeRmf71JsQrSucIS59560qyxRnZ3gGKJ7c8AR81DHKqCU3jItKPDm5G+hX1Y3Al4G/nvT6l4GHFzrWqjUwwKunQ0RTAa6+2u1g5kiENQ09nOqpdzsSY8wsZZMbTkPR/MqN8eRGTIO8xFbiBOnusfMWA8Au4JiqnlDVOPAgcGf+Cqp6UlUPANZgrgQDfc5/puaW6T9jdSFnvdkkNwZO9BEjRGfn1NdaVzvf3X0XrPrSFGfJDQNkkhuZ8rHJ6mWM0Yj9qRhXzXhyknn+zczjh4B3ijiDJ0TkPcAJ4FCZ4q0+Bw/yIk5WY/t2l2O5BGvbRzk50o7aTV1jFpVcz42gb8KwFK8/03PD71zwnGA9ANHRwuc0puasBE7nPe/KLCtVSET2icjTmXOJmjcw5HzmmpunXy+b3JjNsJRzRwYA6Fw9dc6LtjVOI/DeHstBmeLsitUAEEmHiiY36iTCWNRb5oiMmaCUk5PcOpkpIAeBNhGpB/4U+Mx0byAi92ROYPZ1d3fPW+BV49Ah9rOdQEC54gq3g5m7jWsSDKUb6b5o2Q1jFpNiw1KyPTeyfcOOswGAyKhdABkACpUXzOYL4DJV3QF8EPiKiGwo+CY1dA4xMOxcE8yY3Ag7/5lnU7lx7qjT8Ltz3dRZVlo6M8NSekvfn6k9ltwwAEQ0SDhY+Fhf74kyGrNZg42rSjk5KbbOZ4Avq+q0U2So6tdUdYeq7ujo6JhjmFXsyBH2e6/jyishMPtp6yvG5i3Osezo03Z2ZMxikkw4h/zJDUVzs6UEnKG12eRGNGIJTAM4N0Py5xRdBZwtdWNVPZv5eQJ4DCjYdaqWziEGRp3v0YVIbpx9zWkE3Lm5ccprobBQxxi9A3bD1RRnyQ1DOg1xgoSDhe9y1HljjMYtuWFcVcrJSW4dEfEBTUAf8EbgCyJyEvgj4P8QkXsXOuBqo4cO84Jcy/bti3sc++YdSwB49ek+lyMxxszGhMqNwPjpa67nRmZYSherAKvcMDl7gU0isk5EAsBdQEmznohIi4gEM4/bgbcChxcs0kViYCyAhxQNDdOvV1fn/BwbLT3ReK7LqSLv3NJS8PU23wB9w9M3MjW1zZIbhqgzXTXhUJHKDV+UsbgdSIyrSjk52Q18OPP4fcDP1XG9qq5V1bXAV4D/S1X/a7kCrxbnX+qhO9m6qPttAKx58wr8xHn1QNTtUIwxs5BKZhqKhor03MjMzKCZU1ur3DCQG6Z6L/AIcAT4nqoeEpHPisgdACKyU0S6gPcDXxWRbH+uNwD7RORF4FHg86pa88mN/kiYJv8YnhmuIsN1zud0bHjqhAXFnDsvNDBM4+rCZSGtgRH6RqefgtbUNrsdb4iMKSDTJDfiXEg0lTcoY/KoajJTbfEI4AW+kT05Afap6m7g68C/iMgxnIqNu9yLuMoMDvLCBad1+aKdKSXDt/4y1nOCV09YWasxi0kqOywl6MPjH//8Zqs4ssNSsmbTxNBUN1XdA+yZtOz+vMd7IVPyM3GdXwHbFjzARWYgFqIlMApMHTqSr64+k9wYTAKl3SQ91+un03sRPAVbm9AaGqM3UjebcE2NseSGITLsHHTC4cKv1/kTjI5ZltS4q4STkyjOXZfp9vGXCxJctTtyhL3sRES59trFPSyFQIDN4S5ePbfZ7UiMMbOQSo4nN8TnxUeCJH68vsywlEnJjagVZxmzIAbi9TTXz/wByyU3hkufuejcYB2dof6ir7fVRTk00l7y/kztsWEphshgHKBocqM+EGcsuYg7CBpjLs3hw+xlJ2/YkKBx+hs1i8Lmjn6ODS0lbUPyjVk0knmVG3g8+HF6bGQrNyb3DYtEF3ki1pgKNZCspzkUm3G9ugbnsxkZmUVyY3QJnQ3F+7+3NsbpSywpeX+m9lR8ckNEbhORV0TkmIh8qsDr94nIYRE5ICL/LiJr3IhzMcslN+oL/znUB5KMpqZOyWSMqQ16yElu7HxLdRT7bd6QIqohuk7bmHxjFotc5UbID14vPpxKjck9N7KiM197GWPmYCDVSHN9fMb1ssmNsZHS7ySci7XS2VJ8TFlbc4pebUHT9v1tCqvo5IaIeIEHgNuBLcAHRGTLpNVeAHao6lXAQ8AXyhvl4hcZcu5+ZBv/TFYXTDGaLlLWYYypeq+/0MNFlrFzV0V/ZZRs83ZnvO6rT3a7HIkxplTZ5IYv5AOvd0rlRl1ocuVGdRyvjKkoqRQD2kRz/cxNQgP1fjykGCuxcmN4GEa0gc724vtubYEkfka6ramOKazSj/y7gGOqekJV48CDwJ35K6jqo6qanUH5aQo0BDLTi2S6GIfrCyc36kPOXU4r4TamNu19yUkG7NrlciDzZPPblgLwypM9LkdijCnVjJUbmeSGjwQ+EkRjNizFmHk3NsYAzTQ3zpywkGCAOsZKngr23AknYdHZWXyd1jbnc917crikfZraU+nJjZXA6bznXZllxdwNPFzsRRG5R0T2ici+7m67Y5c1ntwoPHtAfcg5gI2NFXzZGFPNxsbY270WvzfFVVe5Hcz86Lx+I40MceTFmctqjTGVIZm5mevxysTKjaBz7lIXdi6glnOesESJxG1GJGPmW2JglFEaaG4q4Y5nMEiYSOnJjZcHAei8rPjMKm3LnM913+vF+3KY2lbpyY1CafeCnxAR+RCwA/hisZ2p6tdUdYeq7ujo6JinEBe/XHKjofCJQPaEYXS0bCEZYyrFK6+wlx1cvXaQYJVMmiQd7Wz1vczB4zbczpjFIpVUvGQzHJ4plRvBIAhplstFwhIjGq/0U1xjFp/B8051RUtTCQmLQLZyo7R9nz3qrNi5rnifv9blzolI3xkblmIKq/QjfxewOu/5KuDs5JVE5Gbgz4A7VNVaSM1StotxqKFws8D6OucAZpUbxtSe9EuH2ccOdu5wO5L5ta39PC/1LEetJ5kxi4KT3MiUwucNS8n23BC/jzrG6PReJOSJEU1U+imuMYtP/3nnMqu5tYRhX8Ggk9wo8frhzAln36veUHxattaVzk2J3nNWeWkKq/Qj/15gk4isE5EAcBewO38FEbkG+CpOYuOiCzEuepFRp7Qs3FgkudHo/JmMDiTKFpMxpjK88mQPwyxh5zura+q1bRvH6Es2ce6sZTeMWQxSKXIJjfxhKd5ApurU76eDbtYHugh74kQS1TG7kzGVZOCik1Robi1h2FemciMSLW3fXaeVBoZZsq6t6Dptq50eYL0XZm5oampTRSc3VDUJ3As8AhwBvqeqh0TksyJyR2a1LwINwPdFZL+I7C6yO1NEtnIjvKTwGLe6Jmf5aLeVbhhTa/buc+7O7KqSaWCztl4TAODgE/0uR2KMKUUqpXglM86/QOUGPh8/42b+su1vCXnjRBPWc8OY+TbQ43zumttLOCfIVm5ESmvue+a8l5WcQZYWbx3Qusap6ujrtVkOTGEVf7aqqnuAPZOW3Z/3+OayB1VlclPBttcXfL2+xbkIGOuNAE3lCssYUwH2nmij3hvhiiuqqz/Fthvb4G/h4GM93PqBVrfDMcbMIJmUCcNS/Di3g/MrNzZwAuouJ+RNEEkWb0pojJmbgV7nM9jcUcLnK1O5MVDitMxdvSFWeV6D+iuK73JpM0sYpKfXZkMyhVV05YYpj1xyY1nhsvP6Vqd5z2iPNe8xpqYkEuzt38h1K87jrbKboG3Xb6GTsxx83obbGbMYpFKMV27kNRTNzpaCL3O/LhQi7E0QTVb8/TtjFp2BPucz2LyshA7j2cqNEpMbZwYbWFXXO/1KgQBt0kfPgH2+TWGW3DBERpL4ieNtay74el2r07V4tM96tRpTS+KHj7Gfq9m5tQqHpHV0sC3wKgdPFK5YM8ZUllQKfJJfuTGp50ZeciPkSxJJBlyI0pjqNjDg/GxeXnxGk5zsbCmxme+OpFJwNtLCyiUzT/Ha7hugZ6hKpm8z886SG4bISJowEairK/h6fbtTjj42YJ2JjaklB39yjhghdl5fXUNSsrat7OVwfydJ60tmTMWbULmR33MjOD4sBXAqN/wJoikblmIcInKbiLwiIsdE5FMFXn+7iDwvIkkRed+k1z4sIkcz/z5cvqgrU/+g4CVJfUfha4YJgkHCREpKbly8CCn1sqpt5irx9uAwvWMlJFdMTbIjarsKAAAgAElEQVTkhiEyliYsUZDC49eyBzCbLcWY2rL3F86Y9l13LHc5koWxbUuamAY5fsQSt8ZUumLJjSmVG8EgIV+KSNoqNwyIiBd4ALgd2AJ8QES2TFrtdeAjwLcnbdsK/AXwRmAX8Bci0rLQMVey3gEfbfQiDSVUPWaHpcRmHkLS1eX8XLV85rsN7eFReiJWdWkKs+SGITIGYW/xk/u6pQ0AjA6lyhWSMaYC7D1UR5unj7VbSrhDswhtfYvTZ+jgI2ddjsQYM5NkSiYkN7LDUnKzpeRXbgSSRC25YRy7gGOqekJV48CDwJ35K6jqSVU9AEyeguNW4Keq2qeq/cBPgdvKEXSl6hkO0E4PBEr4fGWngi1h5qKuU841xsrVM1+attdH6YlX1/T0Zv5YcsMQiQphX/GqjPBSZ9qlsWFLbhhTS/aeXcnO1uPFiroWvS2/thYPKQ7+csDtUIwxM0ili1RuZK+b8ntu+NNE0jYm3wCwEjid97wrs2xetxWRe0Rkn4js6+7unlOgi0HvSJB2b4nfmZnKjXjKN+PwzzNHhgBYtWXmpEV7U4KRdD3RaGlhmNpiyQ1DJCaE/cWPOp7GeuoYZXTY5pQ2plaMDqc5FF3Pzg39boeyYMLbNrJRjnPwkH0VGlPpUinB5xmfLSVXuZGteM8flhJIEVVLbhgACqXndb63VdWvqeoOVd3R0dFRcnCLTc9oHe3+EpMbXi91OD00IjO00uh6ZRQ/cdq3LJ1xt+0tzs3W3hkmVjG1yc7oDJG4l7B/mqoMj4c6iTA6Wr6YjDHuen7PedJ42bmj1HPARcjrZVtzFwfPtLodiVkgJTQSvE9EDovIARH5dxFZ40acZmaptExfuZE/LCWYJqIhtIoPX6ZkXcDqvOergFLHIl7KtlWpJ1pPW2DmGU0AEKHO5wx7H5th0rUzpxKs5AyeNaunXxFob3M+2D3d9gE3U1lywxBJ+AiHpq/KqPdEGJu5gbExpkrs/dkgADtvKTxFdLXYtm6E45EVjI7YSVK1KbGR4AvADlW9CngI+EJ5ozSlSqXB68l8TvN7bkyu3AiFCAWUNF6bCckA7AU2icg6EQkAdwG7S9z2EeBdItKSaST6rsyymqQKvbEG2kMlJjeg5ORG11kPq+iC1TMnN9o6nMvXntercJp6c8ksuWGIJP2EQ9Of2Nd7Y4xGZm4IZIypDnv3Cat5neVv2+h2KAtq6zV+FA9HHr/odihm/pXSSPBRVc2eIT+Nc2fWVKBkyoNXxpMbRSs3gkHCQeeGjY3JN6qaBO7FSUocAb6nqodE5LMicgeAiOwUkS7g/cBXReRQZts+4HM4CZK9wGczy2rS0BAk1Ud7XelJheyw9xmTG71hVvq7obFxxn22dzqf9d4uu+tqprLkRq1LJomkg4TD069W748xEp15KidjTHXYe7yVncED0NbmdigLats7nfG9B3963uVIzAKYbSPBu4GHFzQiM2cplQmVG9nkRsHKjZDzMDJmFVkGVHWPqm5W1Q2q+leZZfer6u7M472qukpV61W1TVWvzNv2G6q6MfPvn9z6HSpBT4/zs72h9Kxhnd+psJqu50YqBaeGWljXUlqPr/aVTj+dnjOxkuMwtcOSG7VuaIgIYcJ10/8pNAejDMRCZQrKGOOmvj44PryUnSuqf2jxhts3E2aMA3vtFm8VKrkZoIh8CNgBfLHozmpkNoRKlUoXbihaaLaUbDVqdLj4THDGmNnJJjfaGuMlb1MXdHr6TVe5cfo0JNTPxs7SKkJaVznT0/ecs8+3mcqSGzVOBwbpoZ221ul7brTURemP15cpKmOMm/Y945yM7Lqu+qd/9jY3si34Ki8cbXA7FDP/SmoGKCI3A38G3KGqRW8F1spsCJUqlS5cueHJnsnmDUsJhZ28VmSo9IswY8z0cpUbTaUnFeoCMw9LOXbM+blhXWmzMvrbm2imn56LNoujmcqSGzVu5OwQEepYtmz69Vrq4/QlZ5572piFUsKsB0ER+W7m9WdEZG1m+S0i8pyIHMz8fEe5Y19snt3j3JW+7l3VPSQl67oV53m+dw1pO0+qNjM2EhSRa4Cv4iQ2rPFKBUulPXi9meRGpnLDSxLJ1ufkD0vJJDeiw9ZR1Jj5kp16tb259M9VKZUbx15yKic3vsFf2k5bWminJ5dsMSafJTdq3IWTziC4ZZ3TNwttaUjSn26yadWMK0qc9eBuoF9VNwJfBv46s7wHeLeqbgM+DPxLeaJevPb+Ms7lvEzT9Ve5HUpZXLctznC6gWMHrPN6NSmlkSDOMJQG4Psisl9ESp1FwZRZUj3jlRsi+EiNTw0LE6eCzfQRi1hyw5h5k6vcaCv9YmBJyKmeGhgovs7xAyOEiLDiypbSdrp0qZPc6Cs08tDUOusQWeMunHYOOstWTZ8tbWlKk8LHyLDSuMQOJqbscrMeAIhIdtaDw3nr3An8ZebxQ8B/FRFR1Rfy1jkEhEQkOF35ea3be7SJd3qfhE2/5XYoZXHdTUtgNzy/+zSbt1/udjhmHqnqHmDPpGX35z2+uexBmTlJpYVA3i2593p/QFNrAPiYsyC/ciPTRyw6YskNY+ZLT7fiI8mSltJnT+xocq4zpmtTdOzlFBs4jmftZaXttKGBdu8AZwZmnjbW1B6r3KhxF844X/zL1k4/XUpLJpnaf8bubBpXlDLrQW6dzB3bQWDyuIr3Ai8USmxYs0DHmTNwbrSJnWsu5HXqq25XvvcKgkR57tFht0MxxhSRUg8+7/gd4zf79/Hny/9hfIX8qWDrMj03rHLDmHnTezFJG71IfV3J29Qva6Bexrhwofg6x0752cBxuKzE5AbQXjdG72iw5PVN7bDkRo3LHmyWrZv+QNXS6pwo9HeNLnRIxhRSyqwH064jIlfiDFX5/UJvYM0CHXtzzURrpwGFf/Vyrgq8wnOHbEYoYypVSvN6boDTSdSXV4C8dSvcfz+8613jlRtj1d8U2Zhy6bmQpp0eqJ/FBAPt7SyVbi4W6WikCscvNrDRcwI6O0vebduSON3RxtLjMDWj4pMbJTQRfLuIPC8iSRF5nxsxLmYXup0/gY710x8gWjqcE4j+czZdonFFKbMe5NYRER/QBPRlnq8C/ifwv6rq8QWPdhF75uF+fCS4+palbodSVtdd1s3zPautr5AxFSqV9uDNP2v1eicmN3w++MxnoKmJcINTdRYZseSGMfOlp1vnlNxYlj7HxQuFv1zPnYNIMuBMA+srvVvC8tYEkXSIwcHSQzG1oaKTGyU2EXwd+Ajw7fJGVx0u9Plpoxd/ePoDSi65cd7aFBhXzDjrQeb5hzOP3wf8XFVVRJqBHwGfVtUnyxbxIvXk40mu5XnCb7vO7VDK6rodwqA2cfSJc26HYowpIIl34kg5r7fo0LlQvbM8OlY7FWjGLLSe7jRt9DLjFIv52ttZygUuni+caMxNA/uGwKxiWb3C2d/p0zOsaGpORSc3yGsiqKpxINtEMEdVT6rqAcD1b7Cjt32MD111gJERtyMp3YXBEMt8M8+l1NLplGv3Xyx9bmtj5kuJsx58HWgTkWPAfUC20uteYCPw55nZEPaLSG2VJZQoHoe9J1p5a+h5uOIKt8Mpq7f+pnOy9svv2JmSMZXIGZaSt2By5UaebHIjMmqlWMbMl94+cSo31qwpfaP2dpZyseiwlGMvOL2uNuxsnVUsq9c4l7Cnj9lNVzNRpSc3SmkiWLIFbRg4OMgXHrma//fgVXz3My/P774X0IWRepYFp5mfKaNlhdNwtL/HSjyNO1R1j6puVtUNqvpXmWX3q+ruzOOoqr5fVTeq6q7szCqq+n+qar2qbs/7V+Rrtra98AJEUwHeeuUASG3NinTFnZfTTjePP+F2JMaYKVIpUnjx+fKSFdMkN8JLnOaiVrlhzPxQhZ7hoJPcmEXjz2xyo7vXQ7rAx/HFx/qpZ4S1N66dVTyrNzk3XU8ftkbgZqJKT26U0kSwZAvZMHD48ef5DncB8M2/HYSxxTGryMVIA8vCMx8YlnTW4yFFf5/dBTGmWj35iFN29tbbaq9JlwT8vL39CE8cm3P+3BizUJJJUnjxevNOCz2e4sNSGpykRzRi5yzGzIf+fqfvTXtoFJYsKX3DTHIjmfIwUOBe6nP7vVzDC3ivvXpW8XRevgQPKU4ftV6AZqJKT26U0kSwIjz4jTFGaeDOa0/zi9gbOfE/9rsdUkkuxJpZ1jDzDCie5iU0M0B/fxmCMsa44smHh1jPcZbftt3tUFzx9rckOZlYyetPnXE7FGNMvkQik9zIWzZN5Ya/zo+HFJFIecIzptodOeL83NQ5y0qJTHIDmDIdbCoFL5xu57r6V6C9fVa79a1cxgrOcvqUVWeZiSo9uVFKE8GK8E9PrGdr4NX/n707j4+iPh84/nlyBxJCSDjCHQ6RQwRBwFvUKljrfWu1/dWirVqtpVXrrbXVaq3WqtRWq631wBsVFBQERREIhCOcCWe4j5D73H1+f8wCARNy7s5s8rxfr7yyOzO780wm853vPvs9ePZf8Qh+/vOa1/+0UFoKhf4EOidX1L1xYiLJ5JFXUPO3JMaY8KYKc5cmcmLEPBg50u1wXHHq9ekAzPnHSpcjMcYcorKSKqKIrJ7LOEJyQ+JiiaOMsjJruWHqNfNirIi8FVj/nYj0DizvLSKl1cbrmhTq2L1i+XLn9zF9G5gxrJbcOHzcjVWroMQXy4i+dXeP/560NHqwmc1bvf95y4SWp/8j6jOIoIgcLyK5wGXAP0QkK9Rx+n3K4rzenN0vhx7DUxkRtZS5y7zfrHtHrjM4aKeu9Zh6KTaWZNlnyQ1jWqhVq2BHSSKnpm+GuDi3w3HF0PN7kxRRwOwvqtwOxRhTXU3dUo4wWwoxMcRTSmlp6xo7yHxfPWde/BmQp6r9gL8Cj1dbl1NtvK6bQhK0By1bqiRSQM+j2zTshW3a0CnGma/18ORGxjznc8iIUY34bNGxo5Pc2NU66yumdp5ObkC9BhFcoKrdAwMGpqjq4FDHuOW7XMqIp/9Q54IfkLqH7N3tQx1Gg+1Y7gyq2jm9fgVVcmwpeYX1n4PaGBM+Pn3f+Tbm7HOjXY7EPZFRwtj0DUzLHYIW1d1dzxgTIoFuKVH1bLlBTIzTcqPckhum7pkXA89fDTx+BzhTpJWNql2H5UuqGMJypHcDZkoBEKFTB+cLg+8lN6btpC1FDDi3b8MDioqiR/weNue3Q62BlqnG88mNcLB2mjNJc/9T0wDol+5jU0UXyou9/e3fjpV7Aeg8oH6JmOSESvJKGjYPtTEmPHz2TgFHs5Jel7TOLin7XXhpNFvozsJnv3U7FGPMfvvH3Iiq9nkzPt75qUlcHHGUUWoDipr6zbx4YJtAq/F8ICWwLl1EFovIbBE5pbadBHVGRpepwrLlwhCWN2wa2IDUThEI/u8nN+b7GEYmkWNPbVRcPZILKfPFsGdPo15uWihLbjSD7ExnhoH+ZzkXfL+hbfATyYYvN7gYVd22r3UGBeo8pH4zxyS3V/LKG9gczRjjeaWlMHtpMudEzYQxY9wOx1Xn3XEUkVTxwav5bodijNlvf7eU6smNf/8bHnqo5u07dKBDZAHbt1lyw9Rr5sXattkG9FTV4cAdwOsiUuNUIcGckdFt27bB3vwojmFZo5IbkR07kBqVf0hyo7wcFm/pyMhOm6B941q79+hYDsDmzXVsaFoVS240g7Xro4iljO59YwHod1JnALJne3vE/Q05PqKopOuItHptn5waQZ4/CfXZyMTGtCRz5kCZL4Zzhm6D2Fi3w3FVSqdITu2+jg/WDISCArfDMcZAtQFFq30GHTEC+tbSnF2EEV23kbGjO36rsrR29Zl58cA2IhIFJAF7VbVcVfcAqGoGkAMcFfSIPebAYKKNTG6QmkqniF2HJDdmTi2jxB/POac0fkqjHt2dHJUlN0x1ltxoBmu3J9I3fhsRgb9mvzOdC3/togZOlxRi67bE0Csyl6jEWpp1Hia5cyxVRFO8oWU1tzOmtZs6uYhYyjjt4pS6N24FLrw0mhU6iNWTZrkdijEGah5zow6jjquiwJ/I6oyi4MVlwkF9Zl6cAlwfeHwpMFNVVUQ6BgYkRUT6AP2BdSGKO3Seegq+/rrW1cuWOb+HxGZDY1qlpKbSyb/jkKlgP3hpNwkUcsZ13Rv+fgE9B7YFYHN2eaPfw7Q8ltxoBtkFHemfcrDDV0paDEkRBWRnuxhUPazb3Y4+bXfUvWFAcjenS0reyu3BCskYE2I+H7z9DozjU9pceLbb4XjCJb/pTQQ+/v1CmduhGGPgYLeU6PpXW0eP7wDAd29vClZUJgzUZ+ZF4CUgRUSycbqf7J8u9lRgqYgswRlo9CZV3RvaIwiyuXPhN7+B8eNhyZIaN/l8htI1Zhep6YnQmHFWU1PpVLWF7dudlhZ+P3z4ZRLnyjRix57Y6NA7ju5DLGVsWJzX6PcwLY8lN5rIX1FFTmVP+nc72KxKBPq120n27iQXI6vb+uJOpKfUv3VJci+nm2HeGmu5YUxLMXs2bCtI4OoeX8PgkE825UndugvnDcjm3xtOp2L1erfDMcbUNKBoHY6+ZDCJFDB/jiUpW7t6zLxYpqqXqWo/VR2lqusCy99V1cGqeqyqHqeqH7l5HEHx+OMsb3cid/MnNoy7CfIOTRTMng2ffibcWvEXuP32xu0jNZXjWUBOjrBsGXw3t4odxYlceOwGSExsdOgRQ4cwhOVkLvI1+j1My2PJjSbKzdjhTAPb/9Dl/ToXkl3SFa929izIV3b7OtCne0W9X9N1cDIA61daRcGYluKNFwtJoJDzftbZ7VA85aZ7UthJZz68y2ZNMcZ1lZVUEd2g5EZEageOj8/iu1Xe/qLJGNdkZfHPjzozsng2jxXdwtHbZ/HI+Ln4ArmCigqYOCGfHmzitos2w4QJjdtPaio/5d/Ex/n529/gyd9uJ4Zyzr1raNPi79uX4yKXkJGTbNPBmgMsudFE2d853VH6HXPouBX90n1s0F5UbvZmF471GU6ruj796v8vMOz09kRSxfxl9RujwxjjbeXl8M6H0VzIB7S57lK3w/GUs69OpVf8Dp77pLdTwzOmmc2b5wzme8CqVTaIbS385ZUADeqWAjC6zy6W5vekdEvL6klgTHNYdN/73MQkTj3Fz6JFcGG/LO7/7jxOH1HIgw/C8KOKWbgmicd6PE/8v59vXJcUgNRUOpDHtWds46WX4L3vuvOH9n8h6dIfNO0AIiMZ0XU7eeVt2LChaW9lWg5LbjTRmswSAPqP7nDI8n4DY/ARxYZvt7kRVp3WLQwkN4bUf2rXNm2FoTGr+W5Dp2CFZYwJoTdfq2JfWRzXDc6A9HS3w/GUyEi4/bo8ZleeyMzffep2OKFVVeV2BK3CxIlw882BJ36/Mw3zo4+6GpMXPfEE/O4Fp3yKimnYh6tTrkunimguGriSjYv21P0CY1oJf34hv/zwbDrGFzH5/RiGD4c35/fhlcRbyV5SxEMPQenGHXzU5edcPf92SGpCC6hhwyApiVu3/R5VOI+P+M1vcG60TTRiqJP4zMho8luZFsKSG020crWQQCHdR3U9ZPmA453xKVZ+581vYdZlOUmZ9BEd6tjyUKNTs1mwO93pbbN1qzPHfHFxECI0xgSTKjz5QAFDWMZZj9tAojW56a8D6BG7g7uf644WtpIZF959F1JTnY7WJqjWrIGcnEDv1V27ID8fVqxwOyxvKS3lzXuW8urHTl0lMqph1dZxvz2GZ36xirmFQ7n+R9Z6w5j9nr9pKd/5R/HE73bTvn1gYXIy12f9jm0P/4vycReQ/cxUzlv5BHTp0rSdpabCM89wzOL/kMmxTD7+SSJ+fVuTjwHgmJOTiKaCjK8bP6WsaVksudFEKzYlcHRUDhIfd8jyQWc4BUHWMm+OubF+VQVJ7CN5eO8GvW507x0U+BKYN9fHL0Yu4OgHr+Dm8TnBCdIYEzTTp1axfEsHJvZ+Fzl3vNvheFJcvPDAHUXMrzqOydd/4nY4ofHnPzsfsi++2PnkbYIiP9/JZ5SWwraNFbBxo7PC69Oshdq6dayv7M7uMmfQwYZ2SxGBXz1/NDf2/Zx5W3tSUW4d843JXORn4lsjGZ/4Fdfe3+fQlT16wH33ETPtQyJ+dQsHMx9NdN11cMUVHDssgvhp70Hbts3ytrHDBzGE5WTMteSGcVhyo4lW7unEoPZbv7c8qXMcPSK3kLW+/t0+Qmndhgj6xG5B2jVslOLRQ5wWHxefU8S/tp1LeUQ8r3zdj/Ky8KwwlJfD+89tpeyLuW6HYkzI+P3w0C+3k8ZWrnp6dOP70bYC1z/cl5Ep67jl/TPY8V4LLycyMqian8HOCfdSUNUG7rnH7YharLUZB1t15vx1ysHkxvr1nh2I3A37lm4ij4MtTBua3NhvzBltKSeWJVM2NldorUbOkiJ8++o/s57xtqJ9VVwxdgcpuptXH9qIRITo/i8Cb7zh9B9JSWm+9x0yhONYRMaKeBtU1ACW3GiSggLYUt6RgV331bh+cLvNZO1IDXFU9bNubxJ9OjW8mfWAUzqRxD52lCbxh+On8MytOZRoG759blEQogyu7dth7HH7uPiWrvzoB2UUb7L+uKZ1+NcftvPtpu78aeS7xFxgrTaOJCoKXp3WmUJpx4SrC9Ft3hwkukYLFsC39Z/tJfvxd+nGVjq/+Aj9KrLYPW2Bjb8RJGv/OPnA4+zFhejGTRSSQH55LGzZ4mJk3rJ+0aHTUjY6ufEzZ5rrea+vcxb4fDB9OvZp6Mg2bYIBw+N55ZR/uR2KaQ6q3DxyHtkFnfjfddPpePs1od2/CEQ080fPrl0Z3Xkje0viWbased/ahCdLbjTByiXOCPoD+9Vc+RuctpdVJT0OTKnkFfuyd7O2Kp3BAxoeWMQ1V3HhpVGcd04lv513Caf//kQiqeLzf64PQqRBosqiZ+dyfPouMlfEcGvSq8zU0/np+bvdjsyYoNuyrpw7H27D6VFfcd2Uy9wOJywMOr4tj03cw5TycTxx8od4rlCvyeLFcPrpcNJJcN99dbYG0OISJrx3DmXRCTz2GOytSODegt/C/PmhibeVWbvCqTdESRXZ6yO5641jaUch7cln8r+8OVaXG9atOHTq+aiYxlVbu4/uRteoHcz7NpDMePVVOOccZ8oaU6uvPtqHTyOZsbK7JTpbgP9cOoX/5JzMfad9xemv/rRltNoU4cIfJxJFJf99oZWMjWWOyJIbTbDyE+cbgIFndq1x/eB+FZRpHOtWeWsawTmvbcJPJGf8sBFTuorwytsJfPRpNBER0K5THKPTNjFjTS8o8n6hUrllJ3/p9zwn/2o4UlnB3Bte4W8bL+T2tMl8sCSd/H2HfYvTkG91Skv5qOfNPHzWHM99GaTqdMExrVt+Ppx7/C58Ppj0dDmS1sRBwlqR2x7vymXHr+fudTcw/ZpXG/9GjzxC0eDR6OLM5gvucLt2wQUXsCBhLP878TkK/vAM/PWvR3zJS3dkMct3Gk/cuok774RbJ1TwIhPIeHlJ8OJsrXw+1u5oR4/EPNLb7SF7VzveWHksx0dkkMJuPpoe63aEnrEu8L1JDM4NLDKm8bMrjEnfyXc7ekFeHr533mca4yj/NvxanYbSN287rYi+8p2IrlgJixbBBx+4HJVpjFWvL+KX753JaZ1Xct/np7kdTrPq+H8/4lym8trrYjk4Y8mNplg5ZxcxlNPniuNrXD94WDQAWbO91SLgi+k+4ilhzFXNM/XjD86OYKEeR947XzTL+wWL+pUfHbeFietu5swhO1mwoRPD//lLSErikhuSqSSGaU9mHXzBxx/zdPsHuPmC3HolK564ciHnb36OB744lW+n7AregTSQVlZx4Sm7OT59F1U7bbT41mrbNhh/zGZW7O3Me1dMZsDNZ7kdUlgRgZdnpjO4wzYueesyMh6Z2uD3mHz1Bwy8/1ISV3zHCSMqyP7PN0GIFDbf+AfG5v6HUTs/5tq5vyAtcif33VlB+eKaZ+LIyYFfvzyEsTFfc8Nj/QF48LE4UqLyue/toUGJsVXLzmatvw/9u5fSN62ULypOYXNpR3529FzOkFnMXpHquQS5W9Zvb0MH9jCQlUDju6UAjP5hKjn044Off8zpn97JuUzj6ddtavsj+WZRHIKfrXRj/bRVcOutcOmlaKYlPb3Cl1fA7B//i18nvcTtsS/w3hl/x1dy6LdZhQXKFT9PJD6inP991YvIqBbQYqO6gQO5rtccthe05QtvfxQxIWDJjSZYuQr6x+cSlVLz3M+DTnEGzMn6cmcow6rTF1mdOTk+g9jOzTMC8jk39MBPJG8+7+0xK9759Vw+2zmcJ384i4+W9aZz9+gD60b/7jQ6yU4+fOXgh//5907hNwUP8PyU7kx58Mjf7qxeUsbvppzCxUlf0J48nv71hmAdRoM9P+Y/TJmbyrJtHXnv59PcDseEmCp8+E4lx/UvYMnmDrx12guc9b+fuh1WWEpIgE8zOpESV8y4+49nzgP1r0U9d+tKrnzjfNokx3HnzUWslgEM/8lQ3n+tCVNpV1bCRx/B5MmQmYnfD1MfWsDx79/NopgxPPUUfPUVXHC+8gff3QwZEcM9fd5g2rC72fjzP+Dfk8eWLXDNlVVEVZXx6rWfExHtfDOelAS/GbuYaQUnkfFJGI0z4mXffAOzZsHy5aylP/0HRtOvv7AXp65wzvCdnJaSxeaC9mzY4G6onqDKuvwOpLfbS1+cmXuaktw46dI0AC5698cs1WPoGZHL5BWDnevo738Pi9anQZeX5wz6qEphbj5LC3tzYb/lAEx/fTeXf3MbnXxb6Tm6CwV5YdA9ryUqLqbk1jt5e8A9TGjzX7p1KOH0125gUtG1vOj7Py6ZdQsnp2Wz4jtnENiyMrjgxJ1klaTz39sy6NbfmxMdNNV5v+hBMnt56u5doXBVjKkAACAASURBVE8O+/2wd6/7Y/jk5Tn3mX37UIXSvDKqPvjYGV8oP9/d2EJJVVvlz4gRI7QpfHn52oONeunRy2rfqKpKe0dt1vOTZqn6/U3aX3PZvs2voPrYMa8123v6/aond16tXWSbFu0pa7b3bU6F81doT9mox7ZZrVXlVTVu87NjF2g79mn5imwtXbBMB7NMuyfm6dGxOdo3cp2W7cyv9f3vP/tbFXy65d1vdeKILzSSSt00Y1WwDqfe1n6wXOMp1vG9svSotpt1RNRi9ZeVh2TfwEL1wLXemJ+mlg9e4PerzpxRpWcO3qagOoSluuzyh1Wrav7/N/W3ZmG+DojfoFFU6J1DPtbcVz9XzclRraj43rYFBao/u6ZUQfX8hC+0ZE+Jqqpu+ihTR/Gdgup111Tpa6+p/uMFnz74qz1647gNev7gtXp86jrtEbNd06J3ar922/Xc47bqHbeU61OPV+gDFy3RKxI/1mNZrMewRIeSqT0ityio9o3eoFmLDi2Lp07aqKd0XauRVKpTA1NtS5GK+FXw6WQuVf3uu0Nek79so7Znr1484Aj3uUYK5/JBG1NG+HyqvXqpduyoe379iILqk3+q0Kfv362gehSrVB97TJedOEFB9eXBT+r/fvia5uY2bDctytat2p/VetnQVfpbHldQfeXvhY1+O79fddofFupMTtddHQfqk2M/VlBd8fDbegdP6uLbX2nG4MPTQydM0xt5QSs+ma4zfjVFQXXqk1maHJWvcZSo4NMrBi5RUH3i/DlBjSWcy4hg1SHKNu3QR7q9oCnsUlBtF12slw1cqm89ulYLC51b0Gs3faUd2K2CT8eOKdFuHctU8Ol/e93Tsu//ZWX6TNofnXLi+aKQ7da/Zq0uGnSNPskdOqnN7bro/55Vf2kdn4UqKlQXL1YtOjTOiq27dOkvntcPTvqzzjj1YV319xlaWfL9ekVNVjwzXe+If14HsVyTyDtwrxd82pVcvTnyBV3yx48be5iec6TywfUCoK4fYBywGsgG7qphfSzwVmD9d0Dv+rxvUwue9+6er6D6v3uWH3G7u87NVFD9+qnDKo17q/TDP6/SP165RGd+VKSVlU0Kp95evsmpTM9/8JNmfd+5T36joPrTzp/op/fP1dKiQwvQDdNW6DMnvqlXJ3+ivxk9RyvKQ5Ts8fv1q0nLtV/UOhV8OvvtHbVuOuXfTiXz/uOm6BW95zk39Tfz9dNnVimo/mzo/BpzVP6iYu0buU7PTF6oqqobFu/VSCr1+u6fB+uo6sXvVz270yJtR75uWZmvL97iVEg++f3XIdl/c1dMmlIWAHcHlq8GzqlrX+Gc3CgtVZ08aY8e38NJanRmmz7d+VGtmDLN7dBalH07yvSao+ZrBFUKqj3ZoCOZr8fHZOrxyWt0VI8temz6Po2K9Cmo/j7iT1o5f9Eh71F6zyN6M89qO8k/kHAA1Y7s0GNYomfHzNTrOk3TG7p+opfFvK/HsljjKDmwXa+oXP3hiG160Zn5euEx2XpJx9n62mUfaFnurlrjzstTnTNH9R/3btLbOr+hD3OvrqWv6mOP1bj9A0e/qaD64eTmTVyH6oOLV+oQu9+ZpQ9zr97FH/WH0Z85f9MPVT/60Pn/uIW/qb7xhvp+cbOmslNT2amg+oPhtZ/Llq5q9tcaQ5neeVm2Tmr/OwXV1/5Z3LQ39ftVzzhD9d57df0L0xRUu0c4ScFT2yzwzBdRbti4ulSjqFBQvTR1pl7R5kMVfLovz6/n9V2hoPpAyrOqfr+OTcnUrmzRvbOX6tpXvlbdvbvZ47E6xKHmvrVZB0evVlA97/htOnOm1vrZYedbM/X+6D/qUazSi3lXP+76c9UtW5o9Jq/xzV+oJ/OVto8q0CXzmlhW1MOqV+fp6ZGzD7l/g+qg2LX61A1ZumvnoeXJ2oX79M8nfaBjo2ZrP9ZoL9brCe2W6xnpOToiZZ3GUvq994qlVIclb9Afj1qlT962UWd8Uq47qn2UWTivQn98tFMXiZUyPXvoNr3tpAV6z3FT9Y+j3tP7r8nWy07drnERZQqq5/Zcpm+/tE8LChp4sH6/5i/O0e/++Ll+cMsMnX7fHF395VbXisywTW4AkUAO0AeIAZYAgw7b5pfApMDjK4G36vPeTSl4/AWFOqLtCu0r2VpZWHrEbQt3l2n3yC16bNwqLVi/WysKy/TFq2dqSsSeQ/55kyPy9McdPtY17zX/N2T77dxcpp0idumwuJW1tl5oNL9ffzJq+YHj6RCxV2/sN0P/fvrbelqnFQeWp0U7FbafHP2t+iuCm9HJfD1Lr2j/qYJqb9mgs15YecTtq6pUr03/6kCsjx/35oF19xzzoYLq5UOy9JW7V+m792ToF+/m6e7dqjN/Mdn5pu2u1Qe2v/eMuQqqk+9fGrTjO5zfr/rsHev0hJRV2il6j54Yu0BB9dnxTiKrtKhKj45ao2kxu3RnbvBbbzRnxaQpZQEwKLB9LJAeeJ/II+0vnJIb+VuLdN4b6/SfNy7Qq45aoEkRBQqq/Vij/+j3uJa+87HzrbEJipysUv3Tz3P0qtHZOr7fah3fZZGOi/9SxzFVz+VjvYs/6jfdLlX96qua3+Dzz7XizHG69LRbdPNNf9CKf7ysOnfu9z8sVFaqfv65+h79k+751YNa+ennTT+vlZWqzz6rOmVKrZuUfjZbR7BA28WWaVZW03ZXXSiSG16pQ1RVqZ7ZeamCajTl2ov1ek2PL3XfPufzRnLkPv2Kk5zz/te/6sW8o6B6dEy2gurnk9Y27o8cpiorVW+/XfWyUesVVP/x0DadPuTXCqpvvNqM967163UkzhdVg2LWKKh+9+qR6wkt2a/HrdBIKvWOPu8dqAeNHeqUQzPun6O/4QmtuvteVVWd9vreA//PoHpr8n+1fOO2Zo2ntdch/H7VretK9a3H1+sFA1YqqHaL2KKfPLmifm+Qna369NOqEydqa2oClv3nd7UbmzUhokhffXiDlgehulta4tcHL1+uMZRp+4h9+syDe3TrVtX161VfvGWJjonNUFCNoEoHJW3W03pk61GJWw9cV8OS1+tVY3L0x4MW6Blt5+lJfKVnRc/S3wz8WF97PFfnz1edM6tSX70tQycO/FjPif5Cu5J76OfG2CJtE1UWaIVZqHcMn6m7ttbeymPv9nL9wwkfaSe2H/xMFrtbT0ldoT85aq7ec8IX+sKF03TKL6dpxuMzdNUr3+ri5+bqOzfP1N8On6HHx2Ye+DKn+k/nyF16+eBl+vfb1ujCr0pqasAaFEcqH8RZ700icgLwoKqeE3h+N4Cq/qnaNp8FtvlWRKKA7UBHrePARo4cqQsXLqx9g9xcmDPHmfLP52PF5kQy1neA4mK+nKW8vOtH/PPGhdwwaWSdx/HB7+dz0Z9GkRKxlxitYJt24ZR2S3joV3sYOjqeOc8v48OVR/H+xuOo1Ch+OiyTpDaVxLaPJ7ZdDLFRPmIjfc7vaD8RUu3QDp/Gaf9zVaisRKt87C2IZMfuKD5f2J7Fxf1Z+I9FHDPhhDrjbihV2J5bxZIXv+PfL/mYuv04ijSBXhGbuHFEBpc/fzp9R7TnoRM/48F54xgYk81FJ+6gb7rSpoYugKrgJwK/H3x+wa+Cz+/8+An89ldbpoKvuIw9m4r4PLs3S4v7EU8pE3+4kt+92I+Eru3qPoYdO3n6tvUUR7bjnue7IUntDiz//QmzeGb9jyjl+8HGRZSzfW8sSYHhVyoLSjml4yqWVBzNT4dmcNzgCoiIQCKr/USI84MicvDUVX++/79YFRRnA/Wrs1z1wOOSgio+m5fElM3DGRm1mMEdd/F10bH0TC1lxpJORCY6MS+583VG/fkSBsWt4/wTdlNUFUfhzlKS40uJaxdLVKRCSTFrtiawrSKFninF9E4XunasIj5OOaZXAUMvSIeBA+v8W4pIhqrWfYHUQ1PKAuCu6ttW3662/dVZPgCffOJ0YaxezPv37kNXrnLOS+Dc+P2K+jmwzO8PJJUDy/z+g+e0yieUV0ZQXqZUlPspL4PKcj+VFUplJVRWKuUVEeytaMueinbsrExmmx6c8aQzOxjXJZOrzt7DWROHEXnMoMb9wU3T7dkD2dnO42OPhbg4d+NpLFU2nHY9I796inxpz2VD13BsnwLaJ1QRGRVBRJQQGSlEjBlFZHQEcXFw8cV1v21zlg9H2Id7dYgNG1j7ZgbzvvEze30PXlo+hn+e8QY3HLvAmbnmgQfgwQedba+8Et56y6l3xMez+Llv+CzqXH51aiZHn9qJKn8EMXGR9E4r56IBK0iILocOHaCiAioqkHaJTl2logLinVnQpLICYgOzrlRWItFREBV14EYjKIHCB/zOY4kI3HgiIkCc+5NTuDnbCH5QOHCDinDuY4DzXn4/on7nNYFtJDLiwPvh91NUGknu3jYkxZcTE6Usye1Ah4RKEiNLeHt+Tzq0qyK6cC8ztg6hjZRQom2YPaOcIS/dwRlv/pzXFg9hyLCohv0j1EaVz9pdxrSik3lw9hn0Pq0nx7RZx4nyDYndkuh+bAoF2TuJSmpLu17JlG3fhy8qFumQTERxIREREJmUQERFGRHqIyKhDVRVoVU+iItDfX7neUwsREYesl9UD56DAEEPnp/9Vbvqdbxqj1X5fv2Pg/WEmpb7/c5vVfBr4LcftKSUwj0VPDDjJC5s+zmvbRlLdo+xxPXpSteFU4iIFOdmd8MN8MQT0Ls3qvCT8duJ3JBDXKckXvhqCB1kL2PilpCcUEGXnrGk9/JT5I/HX6WkdfJR4Y9Cykr5+b2dYfDgOk9PS69DvP8+lMycR1Wl4vNBfnEUO3ZHsH2HkLMljqy8ruT5nbHxUtjNrf2mMfHtMbQd1r/xf4hWYss/p3LRLzqzwDeCjlF7GdNlI306FdEuSWiXJLSJV+f6jRIiIjh4HxMlAqcMC3z8w+9XfD7n+tlbFENWThwfr+xDvi+Rq5I/5a9fjaTz4NRDA6ioYPmfPuLtN6tYtKEDBeWxtI8p4YwBW7nwT6Ppde5h//+Fhc6gXrVNzasKmzax64ulLP10K8u+K2H1zmTaVBVw1NHClXelk3TNefX621QtW8lXD8/i2+WJZO9uz9riNNaXd2WbvzN+ap6NKpoKxiSvZuyYMkae1Z6uRyVQuj2flTNymf2lMnvn0eTSA4A4KWNo+810Sy4hrWMVnZIriYtTYmMgJsa5LUVFKiKKSOAzUOCzUFSUcvlZefCjH9V5HEcqH7ye3LgUGKeqNwSe/xgYraq3VNtmeWCb3MDznMA235uiREQmABMCTwfgNC9rLqmAt6ZFaR4t8bha4jGB946rl6p2bI43akpZADwIzFPV1wLLXwKmqeo7h+2jPuWD1/7G9WVxh5bFXbdmKx9qE6I6hNvn2u39eyEGt/fvhRjc3n8wYmiJdYjq3D5ntn/bv1v7b45911o+NFP6O2hqSmEdno2pzzbOQtUXgRebGlRNRGRhsL+FckNLPK6WeEzQco8roCllQb3KiPqUD+H6N7a4Q8vi9oyg1yHc/pu5vX8vxOD2/r0Qg9v790oMR+CJOsQhO2vl/zO2/9a7/2Dv2+tTweZCoJ2LozuwtbZtAs3IkoC9GGNakqaUBfV5rTGm5bE6hDEGrA5hTKvh9eTGAqC/iKSLSAzOAD9TDttmCnB94PGlwMy6+soaY8JOU8qCKcCVIhIrIulAf2B+iOI2xrjH6hDGGLA6hDGthqe7pahqlYjcAnyGM9Lxy6qaJSIP44ySOgV4CfiviGTjZFivdCncoHR38YCWeFwt8Zig5R5Xk8qCwHaTgRVAFXCzqvoaGUq4/o0t7tCyuD0gRHUIt/9mbu8f3I/B7f2D+zG4vX/wRgw18lAdojq3/162f9t/i9y3pwcUNcYYY4wxxhhjjKmL17ulGGOMMcYYY4wxxhyRJTeMMcYYY4wxxhgT1iy50QQicpmIZImIX0RGHrbubhHJFpHVInKOWzE2loiMC8SeLSJ3uR1PY4nIyyKyMzB/+f5lHURkhoisDfxOdjPGhhKRHiIyS0RWBv7/bgssD+vj8jIReUJEVonIUhF5X0TaV1vn2Ws9nMuocCmDwrGMsTKk6Wq7tkSkt4iUikhm4GdSKPcfWBfya1tEHhSRLdWO+9wQ7dfVckJENojIssAxLwzRPl0tc2rZvyvnPxx56dq16zY0162b16zb16sb9Q1LbjTNcuBiYE71hSIyCGcgosHAOOB5EYkMfXiNE4j1OWA8MAi4KnBM4egVnHNQ3V3AF6raH/gi8DycVAG/UdWBwBjg5sD5Cffj8rIZwBBVHQqsAe6GsLjWw7KMCrMy6BXCr4yxMqTpary2AnJUdVjg56ZQ7t/la/uv1Y57arB35qFyYmzgmEfWvWmzeAV3y5ya9g8hPv9hzGvXrl23wfcK7l2zNe0bQnfeQ17fsORGE6jqSlVdXcOqC4A3VbVcVdcD2cCo0EbXJKOAbFVdp6oVwJs4xxR2VHUOzqjX1V0AvBp4/CpwYUiDaiJV3aaqiwKPC4GVQDfC/Li8TFWnq2pV4Ok8nHnuwePXehiXUWFTBoVjGWNlSNMd4dpye/9ev7abU9iUE83J7TKnlv2berJrt/Vdt25es25fr27UNyy5ERzdgM3VnucGloWLcI+/Lp1VdRs4Fx3QyeV4Gk1EegPDge9oQcflcf8HTAs8Dtdrxetxez2+uoTNtWhlSFCki8hiEZktIqeEeN9uXju3iNN17+UQdWnyQjmhwHQRyRCRCSHed3VeuHZDff5bGrf+n+26dYfb12zIr9dQ1TeimuuNWioR+RzoUsOqe1T1w9peVsOycJpzN9zjbxVEJAF4F7hdVQtEajptpr7qc62LyD04Tez+t/9lNWwf0mulhZZRXo+vRbAy5MgaeW1tA3qq6h4RGQF8ICKDVbUgRPsP2rVzpHiAF4BHAvt6BPgLTiI4mLxQTpykqltFpBMwQ0RWBb4pbW3cOP+e5aVr167bGrX26zbk5z2U9Q1LbtRBVc9qxMtygR7VnncHtjZPRCER7vHXZYeIpKnqNhFJA3a6HVBDiUg0TiHxP1V9L7A47I/LTXVd6yJyPXAecKaq7r8Ru36ttNAyyuvx1cXz16KVIXVrzLWlquVAeeBxhojkAEcBDR60zmvXdn3jEZF/Ah83xz7r4Ho5oapbA793isj7OE3u3fiQ5Oq1q6o79j8O4fn3LC9du3bdfp9HrlvXrtlQX6+hrm9Yt5TgmAJcKSKxIpIO9AfmuxxTQywA+otIuojE4AxwNMXlmJrTFOD6wOPrgdqy6J4kTrrzJWClqj5VbVVYH5eXicg44E7gfFUtqbYqXK91r8cd7mWQp69FK0OCR0Q67h8EUET64Fxb60IYgivXdqByut9FOIMmBpur5YSItBWRxP2PgbMJzXHXxNVr16Xz39KE/Nq169bV69a1azaU592V+oaq2k8jf3D+IXJxvqXZAXxWbd09QA6wGhjvdqyNOLZzcWaFyMFpRud6TI08jjdwmglXBs7Vz4AUnJF51wZ+d3A7zgYe08k4TcmWApmBn3PD/bi8/IMzsNfman/vSdXWefZaD+cyKlzKoHAsY6wMaZa/YY3XFnAJkAUsARYBPwrl/gPrQn5tA/8FlgX+p6YAaSHar2vlBNAncJ6XBM55SPbvdplTy/5dOf/h+OOla9eu29Bct25es25fr27UNySwY2OMMcYYY4wxxpiwZN1SjDHGGGOMMcYYE9YsuWGMMcYYY4wxxpiwZskNY4wxxhhjjDHGhDVLbhhjjDHGGGOMMSasWXLDGGOMMcYYY4wxYc2SG8YYY4wxxhhjjAlrltwwYUVEhonItyKSJSJLReQKt2MyxniDiPQSkQwRyQyUETe5HZMxxntEpJ2IbBGRv7sdizHGO0TEF6hDZIrIFLfjMQ0nqup2DMbUm4gcBaiqrhWRrkAGMFBV97kcmjHGZSISg3NfKxeRBGA5cKKqbnU5NGOMh4jIM0BHYK+q3uJ2PMYYbxCRIlVNcDsO03jWcsN4gog8LiK/rPb8QRH5zeHbqeoaVV0beLwV2IlTQTHGtFANKB8qVLU88DQWu8cZ0yrUt4wIrBsBdAamhyo+Y4x7GlI+mPBnFT/jFW8C1buYXA68faQXiMgoIAbICWJcxhj31bt8EJEeIrIU2Aw8bq02jGkV6lVGiEgE8BfgtyGKyxjjvoZ8xogTkYUiMk9ELgx+aKa5RbkdgDEAqrpYRDoFupp0BPJUdVNt24tIGvBf4HpV9YcqTmNM6DWkfFDVzcDQwLYfiMg7qrojlPEaY0KrAWXEL4GpqrpZREIbpDHGFQ38jNFTVbeKSB9gpogsU1X7EjWMWHLDeMk7wKVAF5wsa41EpB3wCXCvqs4LUWzGGHfVq3zYL1A5yQJOCbzWGNOy1aeMOAE4JdBEPQGICfSxvytEMRpj3FGvOsT+1p6quk5EvgSGYy3Ew4oNKGo8Q0QGA/8EUoHTVHVbDdvEANOAj1T16RCHaIxxST3Lh+7AHlUtFZFk4DvgElVdFtpojTGhVp8y4rDtfwKMtAFFjWn56lmHSAZKAoOSpwLfAheo6orQRmuawlpuGM9Q1SwRSQS2HKFScjlwKpASqJgA/ERVM0MRozHGHfUsHwYCfxERBQR40hIbxrQO9SwjjDGtUAPqEP8QET/OuJSPWWIj/FjLDWOMMcYYY4wxxoQ1my3FGGOMMcYYY4wxYc26pRhPEpFjcGZDqa5cVUe7EY8xxjusfDDGHImVEcaY2lj50LJZtxRjjDHGGGOMMcaENeuWYowxxhhjjDHGmLBmyQ1jjDHGGGOMMcaENUtuGGOMMcYYY4wxJqxZcsMYY4wxxhhjjDFhzZIbxhhjjDHGGGOMCWuW3DDGGGOMMcYYY0xYs+SGMcYYY4wxxhhjwpolN4wxxhhjjDHGGBPWLLlhjDHGGGOMMcaYsGbJDWOMMcYYY4wxxoS1KLcDcEtqaqr27t3b7TCMabEyMjJ2q2pHt+NoDCsfjAmucC4fwMoIY4ItnMsIKx+MCa4jlQ+tNrnRu3dvFi5c6HYYxrRYIrLR7Rgay8oHY4IrnMsHsDLCmGAL5zLCygdjgutI5YNnuqWIyDgRWS0i2SJyVw3rbxKRZSKSKSJfi8igauvuDrxutYicE9rIjTHGGGOMMcYY4yZPJDdEJBJ4DhgPDAKuqp68CHhdVY9R1WHAn4GnAq8dBFwJDAbGAc8H3s8YY4wxxhhjjDGtgCeSG8AoIFtV16lqBfAmcEH1DVS1oNrTtoAGHl8AvKmq5aq6HsgOvJ8xxhhjjDHGGGNaAa+MudEN2FzteS4w+vCNRORm4A4gBjij2mvnHfbabsEJ05j6qaysJDc3l7KyMrdDCbq4uDi6d+9OdHS026EYEzZaSxnRmsoHO6fGGGNCwe43tfNKckNqWKbfW6D6HPCciFwN3AtcX9/XAojIBGACQM+ePRsdrDF1yc3NJTExkd69eyNS079oy6Cq7Nmzh9zcXNLT090Ox5iw0RrKiNZWPtg5NcYYEwp2v6mdV7ql5AI9qj3vDmw9wvZvAhc29LWq+qKqjlTVkR07huXsUt6QlQXDhsHevW5H4lllZWWkpKS02AJnPxEhJSWlxWeOTSNUVcGFF8IvfgE+n9vReE5rKCPcKh9E5GUR2Skiy2tYN1FEVERSA89FRP4WGJR8qYgc19j92jk1JuAvf4Ff/crtKFq+X/4S/vxnt6MwLrD7Te28ktxYAPQXkXQRicEZIHRK9Q1EpH+1pz8E1gYeTwGuFJFYEUkH+gPzQxBz6/X557BkCaxc6XYkntaSC5zqWstxmgZ69FH48EOYNAl+/nPQGhvUtWqt4dpx6RhfwRlg/BAi0gP4AbCp2uLxOPWG/jgtO19oyo7tnBoDzJkDM2a4HUXL98knMGVK3duZFqk1lMWNOUZPdEtR1SoRuQX4DIgEXlbVLBF5GFioqlOAW0TkLKASyMPpkkJgu8nACqAKuFlV7WvCYMrOdn7v3u1uHMYYb1q0CB55BK69Fnr1chIdV18NZ53ldmSmFVDVOSLSu4ZVfwV+B3xYbdkFwH9UVYF5ItJeRNJUdVvwIzWmhfL5oLzc7Shavrw8axlpzGG80nIDVZ2qqkepal9VfTSw7P5AYgNVvU1VB6vqMFUdq6pZ1V77aOB1A1R1mlvH0GrsT27s2eNuHKZW+/bt4/nnn2/w684991z27dsXhIhMq/Lf/0J0NDz7LPz+9xAXBx9/7HZUpprWVkaIyPnAFlVdctiqmgY0r3FQchGZICILRWThrl27ghRp47W2c2o8zJIbwVdVBYWFsHWr/a1NyHn5fuOZ5IYJI2sDPYKs5YZn1Vbo+OrI8E+dOpX27dsHKyzTWnz6KZx2GrRvD23awNixTvNZ4xmtqYwQkTbAPcD9Na2uYVmNfai8Pm5XazqnxuPCMLnRkLF6PGH/B0RV2Lz5yNsa08y8fL/xRLcUE0YqK2HDBuextdyon9tvh8zM5n3PYcPg6adrXX3XXXeRk5PDsGHDiI6OJiEhgbS0NDIzM1mxYgUXXnghmzdvpqysjNtuu40JEyYA0Lt3bxYuXEhRURHjx4/n5JNP5ptvvqFbt258+OGHxMfHN+9xmJZn40ZYtYqi62/mhivhpJPglnN/iNx6C6xZA0cd5XaE3mNlRLD1BdKBJYH+u92BRSIyioYPaF4/dk5Na+bzQfgNOvsK8HfgP9UX1jJWj/vy8g4+3rAB+vVzLRTjMrvfHMJabpiG2bDhYP8+a7nhWY899hh9+/YlMzOTJ554gvnz5/Poo4+yYsUKAF5++WUyMjJYuHAhf/vb39hTQ6Jq7dq13HzzzWRlZdG+fXvefffdUB+GCUeffUYVkVw+9Se89ZYzYP5Pv7zeWWetNzyjNZURqrpMVTupam9V7Y2T0DhOVbfjDEp+XWDWlDFAfriOt9Ga3U3dugAAIABJREFUzqnxuDBsuaGqc4CapgHcP1aPt0bFPjy5YUwIefl+Yy03TMPsH29DxJIb9XWEzGeojBo16pA5ov/2t7/x/vvvA7B582bWrl1LSkrKIa9JT09n2LBhAIwYMYINdvM09fHpp7yYfBfTvkpg0iRYtQqefjqBO/uex8Bp0+DXv3Y7Qu+xMqJZicgbwOlAqojkAg+o6ku1bD4VOBfIBkqAnzZLEHZOTWvm8zljQvj9EBG+36NWH6unrlkbRGQCzoxL9OzZM/jB5eXhI4JI/JbcaO3sfnMIS26Yhtmf3Bg0yLqlhJG2bdseePzll1/y+eef8+2339KmTRtOP/30GueQjo2NPfA4MjKS0tLSkMRqwpjfDzNn8nrc4xxzDNx4I2zf7owr+mrbX/JYxrVO/+BWMH1ZuGlJZYSqXlXH+t7VHitwc7BjckNLOqcmzOxv4VteDmHaranaWD1n12d7VX0ReBFg5MiRQW/lsWgRjKKcifyFR3KyiA72Do05Ai/db8I3nWrcsXYtJCQ4yQ1rueFZiYmJFBYW1rguPz+f5ORk2rRpw6pVq5g3b16IozMt1vr1bM5PZO6O/lxxhbOoSxcYPx7+u/EUfHv3wZYt7sZoACsjWiI7p8Yz/H7nd5h1TTlM9bF6NnBwrJ4urkYVsCY7Ah9RPM6d3P7lBW6HY1oZL99vrOWGaZjsbOjfH1JTreWGh6WkpHDSSScxZMgQ4uPj6dy584F148aNY9KkSQwdOpQBAwYwZswYFyM1LUpmJpO5HOBAcgPgJz+Bjz9OYCZn8IMlS6B7d3fiMwdYGdHy2Dk1nlG95UaYUtVlQKf9zwMJjpGq6olv9gr3VgLQJ2EHS/faPdWElpfvN5bcMA2TkwNDh0JKCuzdG/b9KVuy119/vcblsbGxTJs2rcZ1+/u7paamsnz5wdnQJk6c2OzxmRZo8WLe5VKOG+6nX7+D5cK4cRAZqXzpO91Jbvzwhy4GafazMqLlsXNqPCEMkxsNHKvHdYX7nL9x1w7lFG+Kdv7W1Zr5GxNsXr3f2KdS0zB5eU5iIzXVSWzsn2fbGNPqlWasYAEj+cHZh95a2raFYcOEb+PGwpIlLkVnjDEmJPYnN8JoOlhVvUpV01Q1WlW7H57YCMy25IlWGwCF+U7Xn86dlGLawiZvzVRrjFssuWEaprAQ2rVzkhtg424YYw5YvNBHFdHU1ALxhBNgfuVwqjKXf3+lMcaYliMMW26Em6IiiJdS2qVEOcmN7dvdDskYT7Dkhqm/qionC5+Y6LTeABt3wxjj2LWLebv7AtSY3DjxRCj2xbNsbRyUlIQ4OGOMMSFjyY2gKywWEqNKadtWnOSG/a2NASy5YRpi/6i4iYnWcsMYc6jMTOYxht5dSulSw1jyJ5zg/P5WR8Nya71hjDEtliU3gq6wNIrE6HLaJgSSG2HUBciYYLLkhqm/6smN/S03LLlhjAHIzORbTmDMCTXfVnr1gi4dq/iGE2HFihAHZ4wxJmQsuRF0hWXRJMRW0DYxgkpiqCyyv7UxYMkN0xA1tdywbikmBERknIisFpFsEbmrhvV3iMgKEVkqIl+ISK9q63wikhn4mRLayFuPLRnbyaUHY06rebR2ERg1OoLFDHemlDbGGNMyWXIj6IoqYkmMr6JtovNRrji/yuWIjPEGS26Y+isocH4nJkJCAkRHW8sNj9q3bx/PP/98o1779NNPU+KhMRFEJBJ4DhgPDAKuEpFBh222GGf++aHAO8Cfq60rVdVhgZ/zQxJ0K7R4aSQAI0fWvs3gYyJYw1FUrF4foqhMbVpSGWEcdk6NZ1hyI7h8Pgp98SS28dO2nXPvLS7wuRyUaU28fL+x5Iapv+otN0Sc1hvWcsOTvFzoNMIoIFtV16lqBfAmcEH1DVR1lqruD3oe0D3EMbZ6WZsSARg8uPZtBg+GKqJZu6IyRFGZ2rSwMsJg59R4SBhOBRtW9u2jkEQSE5W2SVEAFBf6XQ7KtCZevt9EBe2dTctTPbkBTnLDWm7U6fbbITOzed9z2DB4+una1991113k5OQwbNgwfvCDH9CpUycmT55MeXk5F110EQ899BDFxcVcfvnl5Obm4vP5uO+++9ixYwdbt25l7NixpKamMmvWrOYNvHG6AZurPc8FRh9h+58B06o9jxORhUAV8JiqflDTi0RkAjABoGfPnk0KuNXJy2NFcU+6JRXSvn1irZvtT3xkrW/DYFUnSWqsjGiB7JyaVs1abgRXXp6T3GhXQtv20QAUF6nLQRm32P3mUJbcMPW3P7nRrt3B3/uXGU957LHHWL58OZmZmUyfPp133nmH+fPno6qcf/75zJkzh127dtG1a1c++eQTAPLz80lKSuKpp55i1qxZpO4fV8V9NX0CrvEuLiLXAiOB06ot7qmqW0WkDzBTRJapas733lD1ReBFgJEjR1otoSHWrCGLwQxKLwVqT24cfTREiJ+s0nQnMdqxY+hiNIdoYWWEwc6p8RBLbgRXXh6FdCIhqdxabhhXePl+Y8kNU3+Ht9yIjwdrxlqnI2U+Q2H69OlMnz6d4cOHA1BUVMTatWs55ZRTmDhxInfeeSfnnXcep5xyiruB1i4X6FHteXdg6+EbichZwD3Aaap6oEalqlsDv9eJyJfAcOB7yQ3TeP6Vq1nJJUw49sgV2bg46JtWQtbWwc6gopbcAKyMaInsnJpWzZIbQaV78ygigcTk/IPJjWKXgzKusfvNoTyT3BCRccAzQCTwL1V97LD1dwA34DQt3wX8n6puDKzzAcsCm26yQQODpKbkhnVL8TxV5e677+bGG2/83rqMjAymTp3K3Xffzdlnn83999/vQoR1WgD0F5F0YAtwJXB19Q1EZDjwD2Ccqu6stjwZKFHVchFJBU7i0MFGTTPYuHAXJbRl8JiaZ0qpbvAgDSQ3FsIJJ4QgOlOXFlBGHEJEXgbOA3aq6pDAsieAHwEVOMnNn6rqvsC6u3G6s/mAX6nqZ64E3oxa2jk1YcaSG0FVsr0AJYLElBjaJjiNWy25YdzitfuNJwYUtdkQwkRhIURH8793YunTB+aXDIHSUrejMjVITEykMJCMOuecc3j55ZcpKioCYMuWLezcuZOtW7fSpk0brr32WiZOnMiiRYu+91ovUNUq4BbgM2AlMFlVs0TkYRHZf70/ASQAbx825etAYKGILAFm4Yy5sSLEh9DiZS2uAGDwsXXnywePbMNa+lO+ymZMcVNLKiNq8Aow7rBlM4AhgTrEGuBugEBd40pgcOA1zwfqJGGnhZ9TE078gS4SltwIisLdzt81MSWGtm2dZcUlNoaVCR0v32+80nLjwGwIACKyfzaEAx9CVLX6iCPzgGtDGqGBggImRd/KLwJ/+bkpQxhV+j93YzI1SklJ4aSTTmLIkCGMHz+eq6++mhMC35InJCTw2muvkZ2dzW9/+1siIiKIjo7mhRdeAGDChAmMHz+etLQ0zwwsp6pTgamHLbu/2uOzanndN8AxwY3OZK2LB2DgwLq3HTw0Eh+wNrOYIcENyxxBSysjqlPVOSLS+7Bl06s9nQdcGnh8AfBmoCvbehHJxqmTfBuCUJtVSz6nJszYbClBVZjvJI8Sk6MOJjdKPfF9tWklvHy/8Upyw2ZDCAeFhXzJOHr1gu3bYVtlqrXc8LDXX3/9kOe33XbbIc/79u3LOeec873X3Xrrrdx6661Bjc20IH4/K3al0rVtPu3bJ9W5ef/+zu+cNT5LbrisFZcR/we8FXjcDSfZsV9uYNn3hEMdohWfU+MlYdgtpaHd2dxUVOiMeZ6QHG3JDeMar95vvHIlNGY2hCeqLe6pqiNx+uE/LSJ9a3qtqr6oqiNVdWRHG8iu4QoL2UYavXpBly6wvSLZkhvGtHZbt5LtS+eobkX12rxvoHTO3tomiEEZUzMRuQfni5D9zQ7rXf+wOoQx9eTz8Ti/46/zwmpcpVeoZ3c2tx0YAq9DteRGeVj2pjOm2XkludHQ2RDOr202BOBLnNkQTHMrLGSbvxNdujjJjW1lltwwptVbv54c+tKvT/1mz01OhpQ2JWSXpNkIaCakROR6nG9mr1HV/f+w9ap/GGPqSRX8ft7hUqbkHD58nnep6hxg72HLpgfG/QKnhVf3kAdWgwPJjfaRREVBjFRQXOaVxvjGuMsryY0DsyGISAzO4F5Tqm9QbTaE8w+fDUFEYgOP98+GYAMGBkNhIdsqU0lLg7Q02Fbczml6WFnpdmSedLDu3LK1luM0NStcsZkddKHv4Lh6v6Zf1xKy6QcbNwYxMu9rDdeOV44xMCPbnTh1iOpzmE8BrhSR2MCMTP2B+Y3dj1eON5hawzGaJggMJlpKPJVVLWqQy//j0C7xhxCRCSKyUEQW7tq1K6iBFBY7H9/2T17YNqKM4gpLbrQ2raEsbswxeiK5YbMhhIeifB9FvjYHkhvbiwKlaknJkV/YCsXFxbFnz54WX/CoKnv27CEurv4fbE3LkrO4AIB+I+oeb2O/fn201Sc3WkMZ4Vb5ICJv4AwIOkBEckXkZ8DfgURgRqAOMSkQYxYwGedLkU+Bm1XV15j92jk1hgPjbZQST6XPEx8zmqyG7mzfE8pua0WBmVEOJDeiyiiuiA7qPo232P2mdp5J89lsCN63Pd+ZESEtDSoqYE9JPBVEE1NaCkn1/2DTGnTv3p3c3FyCnb33gri4OLp390RLTeOC7FVOy61+A+tfseo3OJY3pnegPHsGscEKzONaSxnhRvmgqlfVsPilI2z/KPBoU/dr59QYDk1uVIX/bCnVurOdqR75JFlY4nx8S0hwnreNKqe4IsbFiEyo2f2mdp5Jbhjv21bgjFqUlnZwAOztdKGnjbvxPdHR0aSnp7sdhjFBl7PRSWr0rXEY55r1G5aAnwg2LCtkQJDi8jorI1oeO6fGcFjLjQqXg2maat3ZTjusO5urCkudwUMPJjcqKK6y5EZrYveb2rWM9mIm+FTZVtoecAYTTUtzFm8jzQYVNaYVy97Vjk5x+Qeax9ZHv6OcW0/26ka1/jfGGONVgeRGCW2o9IfPx4yGdGdzW2FpNPFSSlTgK+q2MRUUV7XWdpDGHMpabpj6KSlhu3YCDnZLAaflhiU3jGmlysvJLk6jX498oAFjbvRzfmdvsm+ajDGmRfH58BFBBbFUhVFyo6Hd2dxUVB5NYmQJ4HQXbxtTyW6fjYNjDFjLDVNfhYVsI43oSB8pKdZywxgDbNxIDn3p26NhMyalpEBSdAnZu2ysHmOMaVF8PspwPmhX+iNdDqZlKqyIISHq4HgmCbGVFFtywxjAkhumvgLJjS5JZYhAp04gopbcMKYVK1u9kVy603dAwxoBikB6Sj7rizvZVNLGGNOS+HyUBloUWHIjOAorYkmsltxoG+uj2B/vYkTGeIclN0z9BJIbaSlOf5SoKOiYXOUkN2wqWGNapU2LdqNE0OfYBgy4EdA7rYL19P5/9u48Ps6y3v//65otM5kskzRp2jRL2zRdUujODgKibCogRzyCekBFjufgcuR4FI9fRXEDPO5HEX6CclQEBBVUELGssndv04UmXZK0TZt9mcxktuv3xz2TZplJJsmsyef5ePQxyT33zFyE5J653/fn+lzQ0pL4gQkhhEiP4eGGltnvydDnt5NvGxZu2IO4dW4aRyRE5pBwQ8QnEm6UBoY2zS8NSs8NIWaxQ/VuAKpXuyb92EWLFYdYiD50ONHDEkIIkS7BIAMYJ9r+kIQbydAfsJNvGxz63ukI4cYJoVAaRyVEZpBwQ8Snt5dW5jGv7OQS3/PKZFqKELPZ4QNGV/yFiyf/VrKozsEATtp2HU/0sIQQQqRLKHSyckPWLUgKd8CO03byYqPTEcKLg+DA4DiPEmJ2kHBDxMXf1U87JcxfoIa2Fc+BblwSbggxSx06loNZBSkvn/xjF64xqj0O7epP8KiEEEKkjUxLSTpPyIbDdnIpdafTuPA40OmN9RAhZg0JN0Rcuk/40JgomWcd2lbgMtNLgYQbQsxSh7vyqXR2YpnC59dFS41jycGG4AR7CiGEyBrDww1saD3B/mLSvKEc7LaTU1Cc4XYb7m5p0C2EhBsiLt3tRvmbq8w2tK2gSMINIWYtn49DnjIWlkyt8mLhQuP2ULN00xdCiBljWLgR/lYkmFfbcNiHhRt5RlW1u8uXriEJkTEk3BBx6e40DqKuuTlD2wqKzHhx4OuXg6kQs86RIxymmurywMT7RpGXByW2Hg625yV4YEIIIdJmVLjh90q6kVBa48GB/eTHcZzht1Gp3BBCwg0Rp+4uo67QNefkVdaCAuO2r1veuISYbXyNzRxhAQtrpl55saiom4O9JQkclRBCiLQatloKgL9fmlwmkvYH8OLAbj+5zZlnnM5JuCGEhBsiTt09RslbYeHJbZFwo7c3DQMSs45S6lKl1D6lVINS6tYo99+ilNqtlNqhlNqolKoedt/1Sqn94X/Xp3bkM1PL9g40JqpX5E68cwwLy7wcClZAT08CRyaEECJtRlduuKW6N5F8vUbTUMfJHzG5+cZFhoHeqVVSCjGTSLgh4tLTa4QbLtfJbUOVG/0qyiOESByllBn4CXAZUAdcq5SqG7XbVmCD1noV8ChwV/ixxcBtwBnA6cBtSqmiVI19pjpU7wZg4dqp/ygXLdQcYiGhQ02JGpYQKKXuV0qdUErtGratWCn1TDjgfCZyDFCGH4VD0x1KqXXpG7kQM8CocCPglsqNRPJ0Gz9P+7Bww55ndPUedEu4IYSEGyIu3f1GKhwt3OiVcEMk3+lAg9b6gNbaBzwEXDl8B631c1rrgfC3rwEV4a8vAZ7RWndqrbuAZ4BLUzTuGetQozEdrXppzgR7xrZwqQ0fObTuOJGoYQkB8EvG/o3fCmzUWtcCG8PfgxGY1ob/3QTcnaIxCjEzSeVGUnl7IuHGyVO4nDxj9TGvW6aJCyHhhohLt9uGmQBO58ltJ8MNWe1AJN0CoHnY9y3hbbF8DHhqio8VcWg6YkYRoqJi4n1jWbQqH4CDO/oSNCohQGv9ItA5avOVwAPhrx8Arhq2/f+04TXApZSan5qRCjEDjQ43BrKjD8RkKr7SydtrhEX23JOncPZ8CTeEiJBwQ8Sl22PDZXWjhhVpDIUbA5b0DErMJtHKg3TUHZX6ELAB+M5kHquUukkptUkptamtrW3KA50tmjsczLN3Y7NNvG8si9YXA3Dwrez48CuyWpnW+hhA+HZueHvc4accI4SIw+iGotlTufFL4q/4ShtPr/F+6XBGCTcGQlEfI8RskjHhhjQLzGzdXgcu28CIbfnGRVd6PdY0jEjMMi1A5bDvK4Cjo3dSSr0D+BJwhdZ6cDKP1Vrfq7XeoLXeUFpamrCBz0ha09znorJwehUX1YuNqq9DTRnzViRmn7iDUzlGCBGHLK3cmGTFV9p4+4yfp915smraXmBcZRj0SLghREZ8opRmgZmv2+fAZfeO2DZUueGVcEMk3ZtArVJqkVLKBnwAeGL4DkqptcA9GMHG8CYOTwMXK6WKwseGi8PbxFR1d9McLKeybHpX5Ox2mG9t52CrY+KdhZie45HpJuHbyDEirvBTCBGn0eGGJ6ubXMaq+BojVZVd0cKNnHwj3JDKDSEyJNxAmgVmvG6/E5djZMdrpxMUIXoHp95QUIh4aK0DwCcxQok9wCNa63ql1O1KqSvCu30HyAN+p5TappR6IvzYTuDrGAHJm8Dt4W1iinRTM81UUlkR9QL3pCws6ORQj2viHYWYnieASGXn9cDjw7b/S3jVlDOBnsjJjBBiCrK0cmO6UlXZ5e03wiJH/skp4fZC43O41zP992Qhsl2mNEuINuf1jHH2n1KzQKXUTRjd0KmqqprqWGel7mA+y3JHpu8mE+RbvfT67GkalZhNtNZPAk+O2vaVYV+/Y5zH3g/cn7zRzS7de1txs4rKJdNouBG2aK6bV/fMB60Z0dRHiClSSv0WuAAoUUq1YFR33gE8opT6GNAEXBPe/UngcqABGAA+kvIBCzGTzKzKjeNKqfla62OjKr7SxtNvNA2NLP8KkFNofA73eqM+RIhZJVPCjak0Czx/so/VWt8L3AuwYcMGiTfj5fPRrQtx5bWPuavA5qXXLyXlQswmzfW9AFSuyJ/2cy2qDPDwnkoCx9qwlMes+BUiblrra2PcdVGUfTVwc3JHJMQsEgqNbCia3eFGpOLrDkZWfKVNZEWUSBNRAGXPIQcvg145tREiU6alJL1ZoJgGt5seCnEVjF1iqiDHR5+EG0LMKs37jctDlacUTvu5FtZaCGLhyObWaT+XEEKINAtXbljMRv+HbAk3whVfrwLLlFIt4SqvO4B3KqX2A+8Mf59WkXDDUTisctJsJodBvINS/ShEplRuDDULBI5gNAu8bvgOw5oFXhqlWeC3hjURvRj4YvKHPHsEuvvppxJX4dhEuMDho7fTmYZRCSHSpfmw8aG1ctH030IWnZIHwMFtPVS/Z9pPJ4QQIp3C4UZhrp+Ovhz8g9nR5HIyFV/p5BkwPotHVkiJsCsJN4SADKnckGaBma2n1QOAq2jsr0uBw08v+eCfHQ2jhBDQ3GrBogLMmzf951q4rhiAQ3tlsrAQQmS9cLhR4DQqDAJ+mSqRSJEVUaKFG4M+CTeEyJTKDWkWmMG6W42TDldxlHAjN0gLBTAwAIXTL1EXQmS+5k4n5Y4uzObpd4SvWlOMiSAHDyZgYEIIIdJrVLjh90m4kUiRFVEcrpErFdpNPry+jLhmLURayV+BmFD3caO9iatkbBZW4AzSSwF4PKkelhAiTZr7i6gs7EvIc1ltigpLKwePypLSQgiR9cLhRr4z3HNDwo2Einzcjiz/GpFj8uP1y2mdEPJXICbU3WZMOXGVWsfcV5AfknBDiNmkt5fmYDmVZYMT7xunhXntHOosSNjzCSGESJNgkAFyKcg3Qg2/TEtJKK8XzASw2EdecLSb/Xj95jSNSojMIeGGmFB3h1FaWDh37JXV/DzoI5+QW8INIWYD3dxCCxVUViTuORfN6ePggCwDK4QQWS8yLSW8Urjfl97hzDTeQYWdsT2q7GY/gxJuCCHhhphYd6dRWuiaP3bJ14IC0JhwdybuKq4QInO11Z9gEDuVNbaJd47TwnIfR4Lz8A1kx5KBQgghogv6Q/ixDbVhk8qNxPIMKhxqbLiRYwniDWRMK0Uh0kbCDTGh7m7jNmq4UWh0Zu7rlNVShJgNmnb1AFC5Ii9hz7moRqEx0bTpxMQ7CyGEyFiRWcoFLuMUQxbTSyzvoAm7aWw5jN0SwBuUcEMICTfEhHp6FSaC5JU5x9wXefPq7ZB3LyFmg+b9xhWjylVFCXvOhStyATi4RVbxFkKIbObxGhe9ToYbsjxpInl9McINaxBvcGxvPCFmGwk3xIS6e00U0oPJOnYuX0GxkRL3dko5uRCzQXOTUWKcyGkpi9YZQcmh+oGEPacQQojUG/AapxYF4R7RMi0lsbx+M44Y4cagVG4IIeGGmFi324LLFH3Zx0i40dMVSuWQhBBp0nzMSo4apLQ0cc+5YP08LPg52CjHESGEyGZDlRvhactSuZFYHr8Zu3lstXSOVeMNJe6igxDZSsINMaFutw2XJXq4kVdsHEj7e+WkRIjZoLnTSYWjE5XAz6vmogKqVAuHjshVJyGEyGaewXDlRiTcCEi4kUhevwW7ZWy4YbeF8IbGrmooxGwj4YaYULc3B5c1erl4/pxwuNEnZYdCzAbN7iIqXdHDzulYlHucg22Ja1IqRDRKqc8qpeqVUruUUr9VStmVUouUUq8rpfYrpR5WSsnlTyGmKFK54cw3YSJIICCfDxPJG7TgsIydCm7P0Xi1HLqEkHBDTKh70EFhjifqfXlzjJS4L/HnOkKITDMwQHNgPpVliV/6eWFRDwf7ShL+vEJEKKUWAJ8GNmitTwHMwAeAO4Hva61rgS7gY+kbpRDZzeMz+rM5nAorfqncSDBPwIrdGj3cGEQqN4SQcENMqNuXi8se/WQmv9QOQL9b3ryEmOmCTUc4SjmVFYl/7kXzPBwPlAwtIyhEklgAh1LKAuQCx4C3A4+G738AuCpNYxMi60WmpTicZiwqOCPCjWgVX+kaizdoxW4dOxU8JweCWAhIf38xy0m4ISbUHcjDlRs93HAUWDERpM8tv0pCzHTHdrQRxJLQlVIiFi00SpcP7fUm/LmFANBaHwH+B2jCCDV6gM1At9Y6ckrQAixIzwiFyH5en/F50J5rwkog68ONcSq+0sIbsuGwBcdstzvC9/dLuiFmNzkjFeMKBqEvlIfLGf1gqRTk0U//gPwqCTHTNe/qAaByReJ7YyxcZpTTHtrckfDnFgJAKVUEXAksAsoBJ3BZlF2jNglQSt2klNqklNrU1taWvIEKkcV84dVRbDkKq/LjD8yIz4ejK76OpmsgnlAOdtvYyg17uJZksEcuEIjZbUYccUTy9PYat678sSlxRJ5pgD6POUUjEkKkS/N+40NT5erihD/3ojWFABzc2Z/w5xYi7B3AQa11m9baD/weOBtwhU9aACqIceKitb5Xa71Ba72hNJFrIQsxg4wMN4L4g9lduRGt4ktr/bd0jcerc7DnRAk3HMYpnbfXl+ohCZFRJNwQ4+ruMi5guQpiL/Wabxqg3ytLOIrkUUpdqpTap5RqUErdGuX+tymltiilAkqp9426L6iU2hb+90TqRj3zNDcZx4PKpY6EP3fZqjJy8HLwrbFL3AmRIE3AmUqpXKWUAi4CdgPPAZHjxvXA42kanxBZLzINxWYDqymAP5jdpxrRKr6UUh+Ksl9KKru8OgdHztjispxIuNGT+IbfQmST7D7iiKTrPmEkwK7i2L8qeRYPfV5Zfkokh1LKDPwEo3y8DrhWKVU3arcm4AbgwShP4dFarwn/uyKpg53hmlstOE0DuFyJf25TVQULOcShZnlbEsmhtX6GFkA6AAAgAElEQVQdo3HoFmAnxmege4EvALcopRqAOcB9aRukEFluqHLDBlYVmAnTUmJVfI2QisquUFAziH1oCspw9txwuNEnFwjE7CaX28W4uo+4gRwK58T+Vcm3eOj3Jf5KrhBhpwMNWusDAEqphzCuouyO7KC1PhS+L3aJkZi25k4nlY52lKpK/JPb7SyyHuHg8ZrEP7cQYVrr24DbRm0+gHGcEUJMky8cZlitYDUFs75yg2EVX4AHo+JrUzoGMugOANbo4YbT+DkP9sm0FDG7ZcwRR8rOM1N3qzHH3lVqjblPnnWQPr+srS2SZgHQPOz7ya5mYA+Xir6mlIq5xKM0C5xYc38xla7k9cRYWNjFoZ6ipD2/EEKI5IqEG8a0lCD+UMacakzJOBVfKeftNj6T26NcT8zJNS5CymopYrbLiCOOlJ1nru7jxtw9V1ns8CLf5qPPn7Ylv+PzpS/BunXw8svpHomYvGjdyKKuZhBDldZ6A3Ad8AOlVNTSAGkWOIHBQZoD86icm7z5vIvK3HT4C+nrS9pLCCGESKIRPTfUjKjcQGt9m9Z6udb6FK31h7XWaWls4ekxqjIcjrEfi+x5Em4IARkSbjCs7Fxr7QMiZedDtNaHtNY7ACk7T6HuduMg6ZoXO7zIz/HRH8jgcMPrhZ/8BLZtg/POg2eeSfeIxOS0AJXDvo+5mkE0Wuuj4dsDwPPA2kQObrbwHTrKccqorJx436laWGkc3g8eTN5rCCGESB5fwIQihNkMVnMQf1BW00uUSLPQSH+N4STcEMKQKeFGSsrOxeR1dwRQhCgoz4u5T549QF8wN4WjmqSnnoKeHnjsMSgogIcfTveIxOS8CdQqpRYppWzAB4C4pp8ppYqUUjnhr0uAcxjWq0PE78i2NjQmKhfHnqI2XTVLjQ/BjTvcSXsNIYQQyeMLmLDhQyljWkogy6elZJJIs9Co4YbTeP80+nIIMXtlyhEnJWXnMqd+8nq6NAX0YnIVxNwn3xHArZ2EMrWm5je/gbIymla/h+CF7zAqN/Rkfr1EOmmtA8AngaeBPcAjWut6pdTtSqkrAJRSpymlWoBrgHuUUvXhh68ANimltmMs93iH1lrCjSloru8FoLIuP2mvUbveOM7s39STtNcQQgiRPL6ACasyTrAtphD+kFRuJIq3NzwtJW/szzQn31i10DsQTOmYhMg0mbJaSsLKzpVSz2OUnTdG2e9ewk2ANmzYIGe3cejuARfdUFgYc588h3EgdbshP3nnPVPT0wN//jOvXPFtzqu1cOWpd/FQ03Js+/fD0qXpHp2Ik9b6SeDJUdu+MuzrNzGOG6Mf9wpwatIHOAs07zcamVWumZO01yg4tZq5HGf/Lm/SXkMIIUTy+IJmbMqoMLCaQwyEklftN9t4+ozQKFKlMZw93/g5e92ZeqVRiNTIlMoNKTvPUN29ZiPcKBincsNpHEj7k7eIwtS99hrBQT+f3v4xnE74w/bF3MS90ndDiElqPmz8nVeuiD1FbdpqaqhlP/sPZEruLoQQYjL8QXUy3JDKjYSK9NOI9NcYzl4QqdyQcEPMbhkRbkjZeebq7rdQaOozFiyPIc9pFMFk5AoH9fX8ig+z+a0CfvYz+NSn4Dd8kJ6//CPdIxMiqzS3WnGZeshLYrZBQQG1Oc3sP55pJWBCCCHi4QtaRlRu+LWEG4kSCTcc+VHCjUJjVcNBr4QbYnbLmMtjUnaembo9Nqqt4zf3y88zwo3+bj+QYeWH9fU8ZruWJVVw7bVQVQU//rGVZ16w8T6tQUVr9yKEGK2500llbicQe4paItTO7eGXzUW43eB0JvWlhBBCJJgveLLnhtUcwh/KmFONrOfpN6aBR6vcyCkwwg2vJ6VDEiLjZETlhshc3R47rpzxj5R5+UZA0NeWlmW/xxXatZuXQ2dxwQVGjnHmmeByeHly4Hw4ciTdwxMiaxx2z6GqqDfpr1NbbTRMa2hI+ksJIYRIsBE9NyxSuZFIHnc43AhPQRkup9AOgFdaVolZTsINMa5un4NC+/ihRX6h8WvU3+VPxZDipzX1uzRdgQLOO8/YZLHAJWf18hSXEdpZP/7jhRAGn4/DgQVUz/Ml/aVq64zqr/27M+x4IoQQYkK+oBmbKVK5ofFrqdxIFI/bqJTOLRxbJW3KsWJjkEGvrJcgZjcJN0RMwSD0BPIodo5/QpNXaKTyfZ0ZdjLS3MxLA+sAOPfck5sv/ycHrcxn299OpGlgQmSXnr3H6MFFdVXyPzQtWW9Me9n/ZnfSX0sIIURi+YNmbJFpKZaQhBsJ5Ak3C3UUjq3cQCnsePEOynRrMbtJuCFi6ukxbovyxg8t8l1GuNHfHUj2kCanvp6XOI/ykkEWLTq5+YJ3G80KX3tN0m0h4nF4SzsA1Utzkv5aeSurmc9R3tqZedPchBBCjM8XMmMzRaalSOVGInkGjFuHK/p7cY7ySbghZj0JN0RMXV3GbVHh+J2X81zGG1dfdzDZQ5oUvcsIN847d2Tf0MpKKLb2srUh9vK2QoiTmuqNdZ6rT03B38ySJSxnL3sbZJ62EEJkG1/QgtVkfB6UcCOxBgZAERrqrzGaXfnw+iTcELObhBsips5O47bINX6FQ16xUR7X35tZy0+1b23mCBWc8baRCbdSsHbeMbZ1VkIos8YsRCY63GBchateX5L8F5s7lxWWBvYcLURLcZVIMKWUSyn1qFJqr1Jqj1LqLKVUsVLqGaXU/vBtUbrHKUS2Mio3jEpeixkCmbMwY9bzeMGBB2WPXrlhN/kY9MmpnZjd5C9AxNTVYZz4F80Z/9fEkmfHjoe+3sw6E9m30+gVsnz52PvWrBhkZ2glgYZDqR2UEFnocBPYGKRsSX7yX0wpVpR10uPLpbU1+S8nZp0fAn/VWi8HVgN7gFuBjVrrWmBj+HshxBT4h4UbVqvGz9jml2JqPF4TDjxGd/wocsx+vH6pehSzm4QbIqauVmPOe1HJBAdKh4N8+ujvy6xwY2+zE4gebqw928Egdvb+rSnFoxIi+xw+7qDK1oopRe8YK5YZweqePal5PTE7KKUKgLcB9wForX1a627gSuCB8G4PAFelZ4RCZD9fyIJtaFoKMyLciFbxlY5xDHhNOFTstV7tJj9ev5zaidlN/gJETF3HjANoUVmUrszDORzk0U9ffwbN8xscZF9PGTnmAFVVY+9ec+l8ALa91JfigQmRfQ53F1Cd15my11uxwQgmd2+J/SFOiClYDLQBv1BKbVVK/Vwp5QTKtNbHAMK3c6M9WCl1k1Jqk1JqU1tbW+pGLUQW8YVG9tzQmAj6s34KcLSKr5TzDJrINY0Tblj8eP0yDUjMbhJuiJi6ThjTOormTbBCQm6uUbnhzqBwo7mZfSyjdn4f5iiFJ8vW52HHw9b6CYIbIQSHPXOpLulP2evNP6OKAnrY83pPyl5TzAoWYB1wt9Z6LeBmElNQtNb3aq03aK03lJaWJmuMQmQ1X8iCzRwON8IfsfyeDFtNbxLGqfhKOY/PjMMUeyUxuzmANyjhhpjdJNwQMXW1BbDjwVGaN/6O4Wkpve4Mmud3+DB7Wc7ymujL2FoscGr+Iba1pKBBohBZzNvrozVURnV56j6cqpV11LGbPfVZf7VPZJYWoEVr/Xr4+0cxwo7jSqn5AOHbE2kanxBZzwg3wj03wufZ2RxuELvia4RUVHYN+Cw4TL6Y9+da/QwE5KKdmN0k3BAxdXVoiuiCggmWf3Q4KKSHnoHMSYt9DU0cYDHLTol9kD9lXge7+ypSOCohsk/zZuM8r3pRCt8uampYofaxp2nM50chpkxr3Qo0K6WWhTddBOwGngCuD2+7Hng8DcMTYkbw62GVG+F2G1kebsRV8ZWKyi6P30yuJXblhtPmxx2YoNpaiBlOwg0RU2cnRrhRWDj+jkPhRuakxY3b+ghiYfmG2FUnK2oDtIbK6Drcm8KRCZFdDm/rAqB6uSN1L2qxUFfWTqu7YGhJaiES5FPAb5RSO4A1wLeAO4B3KqX2A+8Mfy+EmAKfts60cCNWxVfKefxWHJboFckAzhw/7mAK36uFyEASboiYunpM8VVuWK0Uql56vJkTbuzba6zcsmxl7GqSujXGePdsPJqSMQmRjQ7vGQCgek1RSl/31KVG6e3OnSl9WTHDaa23ha+urtJaX6W17tJad2itL9Ja14ZvJVITYop8IQtWszGl0GozerH5vcF0Dmlaxqn4SjlP0IrDEjsocuYEcYfsKRyREJlHwg0RU1efJb7KDcBlcdPttaPjWQ22tRUGBqY/wHHsO2yU5S1bFnufFecZ/Tb2vCZNC4WIpelgEEWIBevKUvq6q04z/oZ3bI49v1gIIURm8WnryaVgZ0BD0bBoFV8pNxCw4bDG/lnm5gRx69z4PosLMUNJuCFi6nJbKaIb8iZoKAoU2gbwhyx4J1q5MRCANWvgy19OzCBj2HtiDvMdXeMWnVSfV4WDAXbvkqaFQsRyuMVMuTqGrWSCCq4Em3d6FaWcYPuLMm1MCCGyQSgEQSzYLJFpKdlfuQHRK77SMQ5P0EaubZzKDUeIIBZ8ck1AzGISboiYujx2iuwDYJr416TQZqQaPRMVQWzaBMePw8svJ2CEMQQC7HNXsGzu+O89ZqedZdaD7D6Um7yxiIRRSl2qlNqnlGpQSo1p5qWUeptSaotSKqCUet+o+65XSu0P/7t+9GNFbIfbc6m2p37xCLV2DavYwY7tcglKCCGygT/cDsIWnpZisRmfH/2DchEpETyhHBy22EGR02H8nN3uVI1IiMwj4YaIKhiEXp+Dotz44l+Xwwg3uida+fvvfzdud+wwqjiSQLccYS/LWL4wdkfpiLo5rezpSE5Xa5E4Sikz8BPgMqAOuFYpVTdqtybgBuDBUY8tBm4DzgBOB25TSqW2gUQWO9xbTHXhRH/YSVBTwyrbXna1FBLM7ot+QggxK0QqBqyWkT03At6sn5aSEQZCdhw5sYMiZ65xMWDALRcFxOwl4YaIKhJSFOfH7so8XKHdeEebsHIjEm54PLBv3xRHN772HUfpophlKyb+9V5R5eawr5z+PnkjyHCnAw1a6wNaax/wEHDl8B201oe01juA0e/8lwDPaK07w6WkzwCXpmLQ2S4YhGbfXKrnelL/4iYTqxb14wnYaGhI/csLIYSYnEi4YRvdUFQqN6YtEIAAVnLtE4cb7i6ZlyJmr4wJN6TkPLNEll8scsX3hlSYa4Qg44Yb/f3wyivwrncZ32/ZMo0RxrbvDWMQy9Y5J9y3bqUKPyYNV6bFZCwAmod93xLeluzHzmrHDvsIYKW6Mj0fTFefZnSj27FFrvoJIUSmGwo3IpUbOeFpKVnecyMTeMLXGBz22BfjnOEWeRJuiNksI8INKTnPPF3hdhVFRSqu/V1OI9wYd1rKSy8ZEzJvvhkcDti6dZqjjG7vLmMsy8+beLpJ3en5AOx+oT0pYxEJE+0XMd5ym7geq5S6SSm1SSm1qa2tbVKDm6kOb+kAoHqJNS2vv+Kiciz42bpRVuYUQohMN9RzwyqVG4kWX7hh/Lzd3fFVXQsxE2VEuIGUnGecoXCjxBzX/oX5xv+WcSs3Nm0ybs8/H1atSl7lxgErOXipqs2ZcN+aty3Agp89W9NQdi8mowWoHPZ9BXA0kY/VWt8b7oa+obRU+rAAHN5prFRSXTdxFVQy2M9cw6nsZPNr8kFNCCEy3cnKDeMEfKhyQ8KNaYv00XA4Yu/jzDN+3hJuiNksU8KNlJScy5XZ+HW1GyWERXPju2JbWGyEIOOGG0eOQGkp5ObCunVG5UYo8W94+44VsDS3BXMcuYy1diFLeYvdb1kSPg6RUG8CtUqpRUopG/AB4Ik4H/s0cLFSqihc1XVxeJuYwOF9RqPg6rXF6RnA0qVssGxnU4MLLW1xhBAio51sKCrhRqJ5+ozpmbnjXGvIzTc++Lp7ZCqnmL0yJdxIesk5yJXZyeg8YlQyFJWPExEPk1dsw0Rw/GkpR49Cebnx9erV0NsLLS3THOlYe3vms6w4zmkmVisr8prZfdSV8HGIxNFaB4BPYoQSe4BHtNb1SqnblVJXACilTlNKtQDXAPcoperDj+0Evo4RkLwJ3B7eJiZw8ICmlBM4l1dOvHMymExsWNRB56CTgwfTM4SEaGuDz30OTjsNqqvhyivhacnXhBAzy+ieGzkO4zRj0CPhxnR5eowfriM39qmbMz9cudErPU7E7JUp4UbSS87F5LQ1G1dsSyrjCzdUYQGF9ExcubEgXFRTGf5fduzYNEY5ls8b4oC/kmWV8S/yXTevi8b+MgYnXjlWpJHW+kmt9VKtdY3W+pvhbV/RWj8R/vpNrXWF1tqptZ6jtV457LH3a62XhP/9Il3/Ddmm8YidGsthyM9P2xg2nGNML9v0cnb+gXp/+wfurPoJZ373Gip2Psm7/H/gkZcXoC+9FL7whaRUrwkhRDoMhRtW4xqj3WFcf/R60zWimWOgOxJuxO6F5yw0qpAH+iTcELNXpoQbUnKeYdqOBXDRhXVunL1ZC8LhRuc4B9SjR9mWcwb33w+hufOMba2t0x/sMI2vtxPEwvKl8dewr1jiJ4SZt/ZJ3bsQwzV2FrG4oCOtYzjlisXYGGTTkyfSOo6paPrZk6y+ro5bvV/Fsu5ULrymlL2Odfxzx0+5omo7vXfdDT/6UbqHKYQQCXGyoWi4P4TTOM3weuXz1XR5eo0fbm7eOJUb4XDD3SehuZi9MiLckJLzzNN2IkQpbVAc51z7/HxcdNPdEWOen98Px4/z+e0f5GMfg3d/oQ43uQkPN/a9avyvX7YmvooTgLq1xpXhPa90JXQsQmQznw+avSXUlPWndRy2889iNdvZ9Hp2XYk6+uctXPhvyzhuLuevf/Dwj825/OpX8NZb8P3vw1+PnsqlRW/Q91+3J23lKCGESKUxlRu5kXAjXSOaOSI9Nxx5sRvKOV1Gnzx3v4RJYvbKiHADpOQ807S1m4xwY86c+B4QqdzoipEWHz+OR+fwYlM1q1bBU8/aeZAPJnxayr4dxjvosrPjHDew9JxSFCF2v9ab0LEIkc0OHQgRwkxNdZobkxUXc1pRI282zSWYJflGsKefD1wT5IQq429PwyVXnQxbzWb4j/+Ahx9WvNG7jOvMD6Nv+lekY6oQItuNbihqD1dueGRBumkb6A2HG/mxG+CbnXZy8OJ2y/uJmL0yJtwQmaWt2zK5yo1IuBGroeiRI7zEeQwGLHz72+BywSb7uQmv3Nj7lon5HKVgZfwNEB2n1LCYA+ypz5IzJyFSoHGz8cdcs8KW5pHA2Ws99Adz2bU9O/5G77x4Iy95T+OnX2zh9Iui9yu5+mr43vcUfx58J9/fdC785S8pHuXspJQyK6W2KqX+HP5+kVLqdaXUfqXUw+GpsUKIKRhTueE0TsS93th9IkR8PP3G+9944QYOB07cuPsk3BCzl4QbIqq23hxKaYfCwvgeEA43untjvIEdPcrfuBibNcQFFxgrwW5W6xM/LaXFyTLLAXCOs1bWaJWVrDDtY/eh3ISORYhs1rjF6A5csy7OY0ASnf0uo/fPK48lttIrGfY/vpuvvXEp76/dwoe+sXzcfT/1KbjqihC3cgd7P3+/VG+kxmcwpr9G3Al8X2tdC3QBH0vLqISYAUb33LA7jSkU3sHsDzdGB6OpFgk3cgvGCTcKCoxwQ6aliFlMwg0xRigE7QO5lNr7wBTnr0i450ZPX4z9w+HGeWf6yc2F9ethp7cW39E4l2yNg9awt3Muy12TDExMJurmnOCtjhICsjS4EAA07hkkFzfz1s5P91BYePU65nGMV/7Wl+6hjE9r/uMjPeQoHz/8Uw1qgs/zSsHP7jWR64B/3/NJ9JNPpWacs5RSqgJ4F/Dz8PcKeDvwaHiXB4Cr0jM6IbKfz2tMTY6EG8pqIQcvnplRuTE6GE0pj9v42ToKxykuKygglwHc8S8YKMSMI+GGGKO7G4LaTGneJCZJhis3egcsUVc27NjfyU5W8Y7LjGZH69eDT9uob0ncVeH2dujy57OsfPK9M1bU+PBpKwcOJGw4QmS1A4dMLOYAamF1uoeCWljNOc7tvLLble6hjOvpr77Kk11ncdvVu5i3LL5jW1kZ3HGXied4O4/897Ykj3DW+wHweSDyLjUH6A43NQdjafkFsR6slLpJKbVJKbWpra0tuSMVIgtFwg2rLRxmWCzY8WZ95cboYDQdBvrD4UaBNfZOTidO3AxIjxMxi0m4IcaIfGYrLfTF/6BwuBHSJvqjLK5Qv9coTVyz1viVW7/e2L65rSphpdj7dhsle5NZBjaibo2RhO/eLO8IQgA0tjqpsbVMbopXEp29up8DA/NpbfaneyhR6WCIL3+ngGpLC5/6v9Mm9dib/t3CqWXH+fKO9xHYtTdJI5zdlFLvBk5orTcP3xxl15hvIFrre7XWG7TWG0pLSxM+RiGynW/Q+POJVG5gteLAg9eX3eEGY4PRMZIdfnoGNFZ8WPLssXcymXCavbg9cnonZi/57RdjDIUbRZOYo1FQgAujAWFPz9i7dx82TpBWhte4qamBQruXzYFVRqlIAux+1Xie5esm3ztjxflzAdjzUuKmyQiRrUIhaOwpYUlRR7qHMuSc9xgrIP3jlw1pHkl0f/l/r/Km5xS+fEMLttxx5kRHYTLBN+7KYT9LeeBTm5I0wlnvHOAKpdQh4CGM6Sg/AFxKqcj/sArgaHqGJ0T2G5qWEpk5MVS5kb2nGzGC0TGSHX56PODAA/Zxwg3AafXh9sZeLlaImS57jzYiaYbCjckcm51OXBipRlfX2Lt3n5hDnsVDRYXxvVKwbnEPW1mbsKai21/1UEAP1WfMm/Rj89cvpYJmdm8bTMhYhMhmTU3gDeWwrCJzJu6u++ganPTz/J8yr++GDob4yg+KWGxt4l9+PLmqjYj3fNjFGSWNfO2F8/G2JibwFSdprb8YXk5+IfAB4Fmt9QeB54D3hXe7Hng8TUMUIuv5feHKjVHhhseX1acbY4JRpdSvUz2IgYE4ww2bH/fgOFNXhJjhsvpoI5JjKNyYN4nkVynm5Rq9LqJlFbt7K6ibc3xEg73lSwK8xdLEhRv1FlazHVW7ZPIPXryYOtNe9hzISchYhMhme8PLIi9fljkd161zizi3cCfP7ypJ91DGePwLr7DVW8dXPnYUq31qV8yUgm99U9OsK7nnE1sSPEIxji8AtyilGjB6cNyX5vEIkbXGVG5YrUblhi97KwliBKMfSvU4PF4VX7iRE8Dtl3BDzF4Sbogx2o4bb06l5ZM7OJYXGM02jo4u6h0YoD6wjLqKkY0+a1dY6KKYzobOKY81IhSC7U1FrDbtggUx+8HFZjazovgEe9pLozZEFWI22feaUX617LSCNI9kpAvX9VDvWcyJxsyp3gj5g3zlf0tZajvIB38wtaqNiLfftISLXJv45p9W0d8TTNAIxWha6+e11u8Of31Aa3261nqJ1voarbWU7wkxRZGeG8Mbiho9N+R0Y7o8XkUuAxOHG/Yg7oBcqBOzlxxtxBhtTR7y6MO+YM6kHjffZTTjHB1udO5upZX5rFwyskHpktVGH4799ZNoXBrDgQPQ789hTdnR+JevHaVusZeBoJ1Dh6Y9HCGy2t4tAxTRSen6qnQPZYQL3mfMlXvh3n1pHslJj33uVXYOLuO2T5zAkjP9q5Pf+HwfbaES/r/P7k7A6IQQInWihRt2vHj82Vu5MdzwYDTVPIPhyo2c8YMLp0PjDo4fgAgxk0m4IcZoOzJIKW0wb3K9KxyuHFyWvjHhxp7XjF4cdaeOfHOrXRMON/ZPfawR27cbt6uXDEz5OdafZvw5bH5p6s8hxEywb79iGftQy5ameygjrL/+FPLo47mnMmNVo2BA89V75rHC1sg//8/0qjYizvyv8zg/51W+92AZvunnvkIIkTJ+n7Gih7KEP++ZTNgZxDtDwo10GvCYcJgGwTJ+w+pcJ3i0Q6qQxawl4YYYo601NKVwg/x8yq1tY8KN+m3G0o11Z+SP2L5oscJEkIaW6SfM27ZqzARYuWbq8wxPubgcG4Ns+qusmCJmt71HClhuPTDJrsLJZ3HmcH7pbp7ZmxkVJY98cSu7B5fw1RtbMFsT9HZqsfCFDx6hZXAuv72rOTHPKYQQKeDzaWz4wHwyzHAoL17/5FaQEmN5BhW5lokTb6fTqJrxZMY1AJHtAgG48UZ44410jyRuEm6IMdo61NTCjYICyk2tHDs2cvPufWZycVO1fuSJUk4OVOUcZ/+JwmmOGLa/Mcgy9uFYXj3l58g5ZwOr2MGmNzOniaIQqdbbC8cGCllW1sWIDsAZ4pKz+2nwV9P4cmIaEU9VIABf/d8STrXu4X3fOzuhz33pXW/nVLWTu75rkqtvQois4RsEK/4R4Ybd5MMbkMqN6fL4zDisE/dichYYP2t3r/RtEtPXcPff+Ph9ZzDw7R+meyhxk3BDjNHalUMZx6GsbHIPLCigXB8ZU7mxuymPOtNeTK6xzQmXFJygoWd6V4dDIXj1DRPr2QxLprBSSsScOWzIf4vNTSVyQiFmrX3hdhbLF/vTO5AYLvloOQBP33MwreN48LZ9vOWt4mvXH8SUk9jO9GpOMZ+/cBO7uxfwl99EWVtbZKVPfhI++tF0j0KI5IlWuWE3+fAEZPWO6RrwWXDkTCLcOJE5S7mL7PXDb3v4OR/n3j+XQ1/mNHMfj4QbYgSfD4735VJpOwG5uZN7cEEB5YFmjh1jRDiwu62EOmdT1KvAtaU97PdWTGvMmzdDe4+NS3gaamun9VwblvfT43fS2DitpxEia+3ZbpS9LluVmd3Wa9+1jEXmwzy9MX0flv1+uP0H+ay17OCqH1yQlNf45x+fSxWHufO/eyfeWWSFxx+H115L9yiESB7fIEa4Mayxu1G5IdNSpqvfbyfPHke44TJ+1hJuiEN8fsEAACAASURBVOkKbtrK746dA8B3Av/B4O//kuYRxUfCDTHCsWOgMVFR1D/5B+fnU+47hN8PHR3Gpu5uOOKZQ13JiagPWVLhpUsX0Xl86leJ//pXUIS4uGgTLFw45ecB2PA2BwCbNvZM63mEyFY7/tFLDl5qz5zcakmposwmLlnSyMajy/ENBNIyhl99s4nGgXK+9oG9KOckQ+A4Wetq+eyqZ3m5pVqaHM8AbW3Q0gKd01/5XIiM5fcztueGWcKNROgK5FHknHilaqfLBoC7Td43xPS88K2XOc48PnGDl6Ms4Jff7Uj3kOIi4YYYoaXFuK0onUKb/oICyjkCnFwOds8e47auMnopU+0So7/F/jemXnr917/CBkc9pWfWTLtHQN27a7Dj4fWnsuMPWIhE2741yCnswrJielVQyXTZFTbc5KVlSVifD77+P3ZOM23m3T+6OKmvdcN3TyUXNz/9rwNJfR2RfFu3GrednaClrZOYoXy+seGG3ezHE7TJ7/00eL3g1XZceXFUbhQbVZfuDukoKqbnodcXkmdy892f2FldcoTf7FqVFZ1qJdwQIwyFGwum8C5UUEA5RqoRCTd21xvPs3Jp9MqMJSuM0vKG7VMrn+vqgtde01zq+SOcccaUnmM462lrOItXeeF1x7SfSySWUupSpdQ+pVSDUurWKPfnKKUeDt//ulJqYXj7QqWURym1LfzvZ6keezbZ0ZjHKnZMe4pXMr3jMytxMMDjv079/M9f3HmCQ+653H71NlSRK6mv5XrHBj40byMPvrFkWtVtIv0i4YbfD26pFhczlM8XpaGo2U9Imwikp9BuRujuNm6LCiduCJdXYqxA2Ncua4mL6fnL8Q28q3Inublw6XluXtNn4H51R7qHNaGMCTfkxCUzHDEKL1hQPYUSwijhRv2WQRwMUL08eliweHU+ihD7d8fxwb2rC5qaRqSGd98NoZDi3fwpIeEGTicXlu9j+/EyKR/OIEopM/AT4DKgDrhWKVU3arePAV1a6yXA94E7h93XqLVeE/73iZQMOgu1tsIJt5PVxc2Qnz/xA9Ikd0ERFxdv5vEdC1N6NXBwEL5xp4Wz1Ktc8sPLU/Ka//7fLrzazi8+m/kfKERsW7ac/FreW8RM5YsxLQWM6gMxNV0dRqhRVDTxvmXVRrhx/KisliKmrv/EAEeD81hda0xvuuiaYvzYeOl36V2pLh4ZEW7IiUvmaDkUIBc3ruopLM+an888jF/6ocqNHX6WsxdzZXnUh+RUz6OKJhoOTPCr2N4OFRVQXW1cUe7o4MAB+PrX4eqVezmdN+H00yc/5igufLsJjYkXnpL5ihnkdKBBa31Aa+0DHgKuHLXPlcAD4a8fBS5SKgPXMs1gO8Lnz6tWZP4ltisv7KXFP48tTx1P2Wv+/LvdtLiLuf3y11Hl81PymqtvPpdzcrdw92OlhIJS152ttm4Fu3HOMdSTSoiZxudTY6elWIz3Ewk3pq77mHFRz1U88Wnb3IVGH6hjxzPiFE9kqQMvGqX8NSuNN65zrizBxiDPvpj5/XMy5TdfTlwyREvjIBW0oObPm/yDCwrIwUdJoW9oekv9Pgt17Iby6OEGZWXUsp/9RyaYBvLHP8LAALv+9cd87eiNfOz8Bs49FywW+OGC7xiBR3Hx5MccxenXryAXN8/99lhCnk8kxAKgedj3LeFtUffRWgeAHiDSFXORUmqrUuoFpdR50V5AKXWTUmqTUmpTW1tbYkefJba/aVxhW3VO5lZtRLz7lqWYCPKHHzWl5PXcbvjGN028jRe56IdXpOQ1ATCZuPm6bhp9VfztR3tT97oiYXp7Yf9+OPdc43up3BAzVbSGopFwIwum6mesrqPGD6+o1DzBnmArKaCENlrbJ95XiFga3zBS+JrTjHOr3Fw4a85bbGxcmMZRxSdTwo2kn7iAnLzEo6UpRAUtMG9q4QbAaYs7efpp4ypwS7uDM3kNFoz+3xmWk8MSWzMNbRPMXX/sMV6Y/wHO+s3NfFV/lcfrazhzlZsnft1LxRu/hzPPnPx4Y7C97UzONb/Kc6/aE/acYtqiBZmjL2PH2ucYUKW1XgvcAjyolCoYs6PW92qtN2itN5SWlk57wNlo+0s9LKCFOWcvS/dQJlR6di0X5r7BQy+Wp2Rqyg++6aZ1oIBvX/I8qmZx8l9wmKu/fRpzOcFPfiBzqLPRzp3G7UUXGbcSboiZyufTRs+NnJNLiUvlxvR1tRo/vKK5tol3zstjPsc41pmZy7mL7NC4y6herzm/Ymjb21d3snVwBR0HMntFyUwJN5J+4gJy8hKPllbz1MON+UaZ9nWn7ODwYbj5ZrCYgvwzDw/dF02tq43OQWfsD3xdXRx9pp7L2x+gslLR8sZR2h1V/L7jAi585N+grw/+8z8nP95YbDYuWtrCrs4FNDdJGXiGaAEqh31fAeEGL1H2UUpZgEKgU2s9qLXuANBabwYagaVJH3EW2rzdwhq2wZo16R5KXK674CiNngW8+ffkvtG2t8Od3zVzJX/k7O+9L6mvFU1OST43rnqdJ5tOoXmfTJfLNpFeVuvXG7cSboiZyufVRuVG4cmpzQ6L0VNNwo2p6z5h/Axd8+K46GYyMc/czrHu5CxTLmaHxgMmilUnroq8oW3nX5aLxsRrvz2YxpFNLFPCDTlxyQDBIBzrzJl6uDF3LuTmcmX+szgc8I9/wOUVOygtM5+cbBzFkjJjxYP9+2Ps8Kc/8b/BT+ANWvnTn2DBaeXw8MOwbRs8+CDccgusXj358Y7jqvcb6fgf78n8xjmzxJtArVJqkVLKBnwAeGLUPk8A14e/fh/wrNZaK6VKw319UEotBmoBWVtzlM5O2NtaxFn2bVBVle7hxOXqWxZiY5Df/s+RpL7Ot24bxO2z8q13Pg91o9tBpcaN/11GCDP33/pWWl5/JlBKVSqlnlNK7VFK1SulPhPeXqyUekYptT98G0fbvvi1txu3S8OfTCTcEDOVzzc23LDbjGaY2TotJdZxI5W62ozqF9f8+Fbym2/roLU/b+IdhYih8biTmryRPc3Wv78GE0HeeLY/TaOKT6aEG3LikgFOnIBAyEwFR2AqlS1KweLF5Lfs4cpwx5Tr7Q9PeDJQW2nE+Q0N0e93P7GRn6l/473vhZqa8Mb3vAcefRTe/3647bbJj3UCS//1Qlayi8d+LZcaMkF4KtongaeBPcAjWut6pdTtSqlIA4T7gDlKqQaMKq7IqktvA3YopbZj9Ov5hNZaTi9Gef114/bsZR3G33IWcL19He9yPMdDz5fhT9JKqYcPw0/uMXMDv6TuzusnfkCSLHr/aVzseImfP1VOUJrgT1UA+E+t9QrgTODmcPPyW4GNWutaYCMnjx0JEZkFW15uzFuWhqJipvJHCzeyf1pKrONGynR1hsjFja0kamH6GPNzu2n1FIydstnXB6+8kvgBihmnobeMmtK+Edvyqoo5xbaf13dmdlVQRoQbcuKSGSJNQBe43EanzqlYvBgOHODzn4cPfUjzrmP3TRhuLFpiNpaDjVG58X/PV9Gli7jlllEnXFdeaVRwOJ1TG+t45s/nnxZv5aWmKk4cl6kpmUBr/aTWeqnWukZr/c3wtq9orZ8If+3VWl+jtV6itT5da30gvP0xrfVKrfVqrfU6rfWf0vnfkale+UcIE0FOOyeOOb2ZQilueE8Hrb45/PmB5Jwx3vq5ACoY4KsXvABr1yblNeKiFP/63hO0DM7lr/ePLmwU8dBaH9Nabwl/3YfxeWMBIxuWPwBclcjXbWszlnC0Wo2+11K5IbJCfz+TbWjk8ymj58bwaSk2I43N1nBjnONGynR3QRFdQ73tJjIvz40vZB15rGlrg/PPh3POMZZvEiIGf1s3TaEF1CwceyXl9OrjvNG+CB3K3HOjjAg3QE5cMsHhw8ZtZfU0fi3C4cbaNZpf3XGUnL72CcMNe0UJVTSxf3eUS69tbfy641JWzT/B2WdPfVhTcfUNhYQw84fvSSGQmPlefaaf1Wwn79zs6LcRcflXT6eCZn52V2/Cn3vjRnjoUQtf5NtUfvvfE/78k/Web5xBGa3c+53MbuaVDZRSC4G1wOtAmdb6GBgnMsDcGI+ZUlPy9nYoKTG+lnBDZAWPByoq4L77JvWwXq+VfPPAyIai4Wkp2RpuDDfquDH6vqQtWtDVazLCjWGh0XjmFxq9mVojM6u1puXCD7N028Pcb74RfvObhI5PzCyHX24hiIWaFWMvdp1xlokuXUTDM5nbdyNjwg2Rfvv2Gbe1K6dx5XbxYhgYMOa47N5tbJtojvq8eZzCLrZuCY25q+WpnbzCOVxzmXvqY5qiVZ++gJWqnvt+KctpiZktGITXd9g5i1eNKztZxLKilhsrn+Zv+xfR2JC4KwkeD9z8iQA1qpEvXPVWQldkmirrogo+uvgF/rx/KUeaZG7KVCml8oDHgP/QWsedik21KXlb28mZnhJuiKzQ2go9PfDUU3E/JBSCTm8uJfaR8/HtVuNYla09NyImOm4kc9GCrj4LLrohL74+GvOLjCTp2LHwht27ua3+GvbrWj4R+imv/nIfMr9RxNK4uRuAmrVjK4VOv9pYPeWNx5pSOqbJkHBDDNlbH6CSJvKWV0y8cyyLw0skHjgQf7hRVcVZvMqexpwxH/oee3AQgGtujnohLalUYQE3rd/CmycWsvVlWaFAzFzbt0P/oI2z5x00GgNkmRs/lYsVH9//r8RN1/jyl2Ffg4W71c3Y7/xawp53um68pYAQZu77klSUTYVSyopxgvIbrfXvw5uPK6Xmh++fD5xI5Gu2t58MN+bMkXBDZIG2Ntzk8r2/rSTgjy807u6GkDYxJ3dkicZMqNyIcdxImW63lSJzH5jju9g2r8z4f3bsqHG7+1eb+SU38JFr+qma6+W6jh8Revb5ZA1XZLnGeuOPteassedeK9+1ECf9vPGPJDU6SwAJN8SQvTv8LGfvsK6dUzA63JgzZ+LmpHV1nI3R4Oj1UYV+v3ujilPtb7FsXRL6asThw99Yhh0P934xc8uvhJiup/5ifPh8xzvSPJApWvDJ9/Jh++/4+ROlJ8twp+HFF+F739N8grt552fqTi5zkQEW3/h2LrFu5J5H5yStiepMpZRSGP279mitvzfsruENy68HHk/k67a1jZyWIg1FRcY7cYLH+Cf+s/92Xno4vtA48ns9J29wxHaH3TjBztZwY5zjRsp0eey4bPFfZJu/3rhI0dpgNIS889flOE0e7vppHl/5Rg6HWMSWn76WlLGK7Nd4QGHHw/zlY6dBmS2K0+Yc4OUD89MwsvhIuCEAo2fU3karEW4sWTL1J1q40LiNhBt1dROvvFBaymklhzCpEK++enLz4UOal7tW8v66+qmPZ5qKLj6N9xf/nV+9vIjOjsxtniPEdDz12ADr2UTZZevSPZSpcTi49RPd+ENmvnvr9OY6nzgB116rWWI9zHcqfwy3356gQSZITg6fuvwAR73F/P6Xie8zMsOdA3wYeLtSalv43+XAHcA7lVL7gXeGv08IrUdWbkSmpUyyT6MQqdXWxg5WAdCw8XBcD4kseVxSODJ1zfalYIl93EiZLq+DInv8P8D801fgpJ9j9Z34+gZ5/MgG/mnpLkpK4PKrbJgI8sQ/ErritZhBGltzWWw/iilGSvC2NX1sHVxBT0Nie8skioQbAoCjR6Hfa5l+5YbDYZS1NzZCff3EU1LC8lZWs8rRMCLceOD7Ru3uh9+XxndEpfjcp/24Q7n86JZD6RuHEEnS2Qmv7sjlMp7Kun4bw9V++VquMz/Cj39VyIEpztjw++G666DzuJ/f+a4k757vxj3HOZUuu/0samjgR9/K7LXmM43W+h9aa6W1XqW1XhP+96TWukNrfZHWujZ8m7CJI729xu/V8MoNn89oTSVExhoWbuzfHF+IOlS5UTwyubPnZHflRqzjRqpePxiE3oCTotzBiXeOOPVU5tHKsQMenr97Dz24eO/VxoXGkhI4p/oIT7SfbcwlEmKUxu451Lhivw2ef1URIcy8fN/eFI4qfhJuCAD2hn8/lztbjE9f01FTA7/7HXR1xb90Yl0dZwVe5PXXNcGg0Zjqlw9aeTsbqX5veq8mn/qFy7kq50l+9OAceuVCqZhhnnnGmCd9efVuWJDS1e0Sq7iYOz5zDEvIxy3/MvmrCVrDJz9prJDys+DHWf3Zi+Cyy5Iw0OkzrTqFTy38M68cKufVf0hTuEwWWTRheOUGSN8NkeGGV24ciq/Pw1C4MWfk9sjCKdkabqRbT3hxLFdeIP4HzZnDfFsHR44q/vCgByf9vPPTK4buvuISL9tZQ9NfdiZ4tCLb6ZDmwOACaubHTuDP/HAtVnw8/9fM/KOWcEMAw8KNxb6Jp5FMZM0aI2r+ylfgIx+J7zErV3KebyN9fYo//hGeew4OthfwkcI/wLJl0xvPdNntfOmmdroCBXzvc4lrWChEJvjDgx7m0M7p16+YeOcMt+Ab/8aXiu7m8ZdLeejXk/ggCNx2G9x7L3zRfBfXn7Uf7rwzSaNMjI/dXk0xHXz7swntfSkSbKhUf1jlBki4ITLb8cNejjMPgP198+JKJoZ+1+eOPLVQeU7seCTcmKKuLuO2qGByQfb68mO8dGI5921fz6XztuMoO7nyxXs+bvRL+PNDfQkbp5gZWus7GMA5bhF/bqGV04v288Le1C/2EA8JNwRghBv5qj9q85hJu+suY/2pr30NbHEuK1tXx/t4lLVLern5ZvjgBzULTEd576We6YctCbDhG1fxfuvvuev+OTQ3p3s0QiRGZyf88Ukr1/Eg5mvfn+7hTJ/Dwed+sZKzeZmPfzQwFNqOR2v46lfh61+Hj1n/j2/U/AIefxys1qQPdzryrruCz7j+jz9tms9OufiWsUZXbkSuaktTUZHJdh7KB2BNVQeN1BA6PPEHn44TQSz4KSjNGXlHeTl2vHj6pAPyVERmjrgKJ9eo5zvXvMkXuIMAFj70gZFh/9L1+VRZj/LcprFLfYrZrfGV4wAsWZkz7n4XrO1ls3clfQ3HUzGsSZFwQwBQvyvEcr0HVTuNZqIRdjsUTbJR0cqVWAlw/1V/oqMDQv4gz4QuwnnxOdMfTyIUFHDXp4+ggyE+9y9ypVTMDL/9LQwGLHyk9mVYvjzdw0kI65WX8/BNz2L393HxOf00NMTe1+OBG280ctgbLL/invKvYXrm6YlXeMoEZjOf/IKTfHq57ebMbOolYocbbfK/TGSwHceMX9irL+rFQy5Ht0y8DFVHq59iOlGuURfJ5s/HjhdvZ/Z2FE2nocqNSX6stq49hTv4It32+Vz19fUj7lMKLqg6yPPHlxMKSndjcVLjdqOXV81p47couODqYoJYePHezOu7IeGGwO83lmA9g9fSNwWktBTmzGFNzws8+yy89qkHWcFeuPDC9Iwniuqv38h/F/6UR56fyyMPhdI9HCGmRWv4xc+8rGEraz+yJt3DSaiKn3yRZ878Mp5OD+esG+DRR0euTqE1/P3vsH695v774f+pb3L/iv/B/MpLUFWVvoFPUvFnPsznC+7hDy+V8srL8gE1E42elhL59Toc3wIUQqTFjq5K5ju6OOtCOwANWyZuONZ+PEgJ7VA4Ntxw4MHbLfNSpqKrw5iOUlQSX++TIauMnikFV14YtTH2hef6adcl7H5aypHFSY37ApgIUn3m+Eu9nndDDU76+cufM++zh4Qbgk2bYMBj4nxegHPSVCmhFKxbB089xXlr+1n893uNZWUXLUrPeKJxOLj17mpO53U+8dFBmZ4istrGjbB5l52PW34BN9yQ7uEklsXCmme/z4sX3MaCvr1ccw0sXxLgxhuNSo1T/n/27jvOqTJ7/PjnTB86DKDogCBYQERQxMIqYEFw7bp2RWW/6squbS2o61p2Vezlt5a1YF9FsaGigAgigiIgHaSJMIBU6dOSnN8f9w5khsxMhklybzLn/XrlleTmJjk3uXlyc3Ke5+msnHwybF2yllH05V/nzUQmfuvM9JRMcnO56ZFW7M1qbr1qAyHLufrOunVOMWP9+s71Ro2c6o09ndHHmESYtaMDXVquocPRTlZu0fzqxzDasD5EHhugSZPyN5R1S9lUg9k+zE7rlzgjijZrXb9mdzz4YLj2Whg8OOLNvS9rDcC4N5bXKj6TWpYsz6RNWgFZeQ2rXC+7fgYntZrH5ws7oCF/JTgsuWH45hvn/PiWP8P++3sXyN13Q0GBU63x3Xdwxx3exVKJzAvP5c1T3yVQWMppvbfa7CkmKanC3beXkM8KrrpKoFXVGfqklJtLx9FPM+Xuz3g5/Rryl37D52+sZ8Sbm8hfNpEXuIbFjbvTd+hFMGyY86szCdX/80U8kP8CkxY259Xn7Z9Rv1m/3qnaCB86av/94ZdfvIvJmKoENm9nrnaky36baL1/JlkUs3hZRrX3W78hzUluRKjcyKGIom025saemDNpC43YTKs/VDHCYyTp6fD8884g/xG0PWF/9ksvYPyEGlaEmJS2ZG0D2jeIbhyN0/oFWB7MZ+57c+McVc1YcsPwzTdKp4yfadm7k7eDdx53HFx8sVNK0ru38xer34hw4LB/Mbz1zcxdmsuZvTZZgsMknU8+ge+nZ3G3PEDOHTd5HU78ZGaScf8/GbjgVsb+YzyrDz+NtZ36MOr4B7nmhcPJXjzXmdHJB4MW77H0dK4c1o/j+YZb/x5kjf/G9qrT1q3bfQiX/fe3yg3jXwunbKKEbLocWEx6Ouyfu5pFa6r+Fxdgw+b0yN1S8vLIlSKKttm01XtixuwMujCLtC6dY/vAIvRpv5zxqw+idHtJbB/bJCUtDbBoy160z49ufzj1Zmests9e9NdMkpbcqOMCAZj4rdI78JWTXPDaY485JfKvvAJpPt09GzSg78R/8karwUycUZ9eh6xjySKrBzfJYf16uHZgCYcyiyv/L8Pp/pXqOnRwpkP5/nv46Sf44gu45pqkrdaoSI49hv9e8i2FxWlcccZG657iI5UlN5Ytc2ZMN8ZvZk1xKsC6HOqUmnfMW8vsTVWPRaQKG7ZkRq7cECEnSykqtIappkIhmLUqj8Ma/rL76xoD554nbKQZnz25MOaPbZLPgk8XsZE8jjw2utni9uncjMMb/Mx7k1uXG9fMaz799WgSZeJE2LY9jd6M90dyo1UrePVVb7vHRKNNGy6efQefdruHXwoy6XJwMQ9eMpfNG6rvl2qMVwIBuPKyAL9vhLf2uZ3Mxx7yOiQTIwe/cCNPtBjCl1Oa8fh927wOx+D8MFm4EPbbr/zydu2cz2JBgTdxGVOVmTMhg1IO7pYLwJHtN7I40JaN6yrPxm3dCqXB9MjJDSAnFwoLk7hCziO//ALbArkctn98SoT7/f0Q9mElL73q76nPTWKMe8+ZDbLPpftGfZ9r+y/np6KOjHvDPwMRWnKjjnv6aWiWtY0/NpoInWNc8pbq8vLoN+0B5jw1lhNzvuOu/x1CfvMiLmw7mTcu/4qCT3+CYhtAy/hDKATX/jnAZ19m8ITcTJe3b4eG1ZcamyTRoAHXfnkW58kH3H5/PUZ8ZGUBXluwADZtgmOPLb+8LHdvXVOMH836OZuOzCdrH2cw0R5dnbEypo7eWOl9NmxwziN2SwFy6qWxo9jGdqipmT863QMOO7z6MU/2REazRlzV9mu+XHoAK37eEZfnMMlj/OQc8tNWsv9x0Sc3Lnu4M3uxhkdu3xDHyGrGkht+tGULBTc/wf+aX8+wva/nl0GPwbYo/4kLBOCRR+C006BvX5g0qdJVlyyBTz5Rrg38h3oXn+UMPmRqRoT8G85lxObeTH10HBd1+JFxKzow4M2TaH1GNw7MWc61LT/kwz+9w475Nvef8caOHXDh2UW88noG/+DfDHrjaGdcG5NS5PBuvPb/tnIE07jo/ADjx/moTrQOKvv6teSGSSazfm1EF2bt7E91RE9nOtgfv6n8x29ZciMvaxtk7l4FcNDeW1hU0oZNm2Ifbyqb+fUG0gjS+YSWcXuOqx52xk0YcunsuD2H8T9VGF/QgT75i5C06Kusctq14oYT5zBqTVe+fGxOHCOMniU3fGb+yF/44z4/0ebJG7lkwzNcuOYZ2j93M2fsO42F31YzUtyWLaw75VIevH0TF04cxLnf3sjjPT9g2V8epmInbFW47z7IkCCD5LlKp4oyUcrI4Ihb+vDioj6sLmnOjC9/44kBMzm4fSnvbOzLucMvomWnPC7aexwf3fKdTYlmEmbit8rhB2xh+IgsHk0fzP2v7weXXup1WCZO6g+6gs9uHEvbwGL6n1zKh+9bBYdXJk1ypn094IDyy1u3dv5LsBlTjN9s3AgFmxvRJX3ezsq+Jofsy0EsYMq0yv8AW7/eOW/eMPKxzWndVxMgk1Gf2sCVNTFzaikHsIh6Rx4St+dod/6RXH/QKJ6behSfvWB95eqqud+sZ10ojz5H13zWtUFvHUOXzHmcfVsHhj20lIDHPfR9k9wQkX4i8rOILBaR3X5pi0i2iAxzb/9BRNqG3XaHu/xnETklkXFHsunHRYw6/xWebPs0D9Z/gJfa3M8P5zxMcMq0Su+zeTP8/ewldPljPt/t6Mo/ByxnxgyYMQP+ceESJmw5jEOPb8Ld16ylsHD3+/88bhXXtBtFm69f5S4eZFqL/szcpx+38DjtX7iFc9tOZcLoIlShqAjuvBPefBNuk8fYZ8DJu3cKNnssLV047JS9uem1wxixuBMbihrw9TtruKz7Ar5a14VzHu9Jy6alXNpuIm9eOZZZr06jZOU6r8P2vVRqI+JNFb6fUMLZR67guOOF4lUbGN32Gm6ZdhFy+WVeh2fibK8nbueb6z+kS/Anzj0/nb9dvsn+Ma1Gde3Lnpg0yanaqDgZT0aG85VrlRvGb2bPcJKhh7XdvGvH3W8/juRHpixsXOmggTsrNxpFnu71Z0GCYAAAIABJREFUqCOVPNbz2YfJmdyIR/tQncJCmLKgIYelzXEGxY6jh0d24bD0OVw+qAHffrQ+rs9l/Gn4PU7lTu9Lou+SUqbR3vX4+vMiOmYs4sI796d1g43c1PsnZk3w6MBDVT0/AenAEmB/IAuYCXSqsM51wAvu5QuBYe7lTu762UA793HSq3vOI444QmMlGFSd9d0Wfey8ydqr0TRNp1SdnxflT81Yrxc1/UJfHTBOp03YpgsWqI4eFdKbr9igjTK3qxDU/8v7QNdOW77bc/w2ZpZelvueguo+jbfq4FsD+uabqs88GdBTD/1VQTWbQv2//it03rxd91v2S0jv6DVRm7FeQbV+donWzw0oqA5Mf1VDrduo/vprzF4LU7XSooCOeehH/b8OX2sz2bBz38ikWDukLdZe9aboJa3G6u1dRuoz/T7XD/8yWn94erKunLRMA8UBr8OvEWCqJmkbEcv2IVG2rN6mX7/ws95z2lTt3NhpExrzu97f7End+v9eVQ0k1/5jaq/ov6/p9ZnPqhDUvOwtetfly3XBnFINhbyOLLbtQ21P0bQvFU/VtRHr1jlt+5AhkW8/6STVI4+M/vUyJt5CIdULj1mmQlB/++/H5W57pt0TCqor5m2JeL+LLw5pGgHddMQJkR/8iy/0Ml7XZo1Kov4q8ksbEY/2IRoD+yxRUB191rO1fqxoLPloph4oCzWTYr3vovm66XcffFGYhJj46kJNp1TPbTOlVo9TtHK9Dj/1FT27wWjNokhB9ZwOM3T2xE0xinSXqtoHcW73logcA9yrqqe41+8AUNWHwtYZ5a4zWUQygN+AFsDg8HXD16vqObt3765Tp06t9PbNm52p2gIFqyndsJVAqRIIQGmJsm27sLJAWbEsyNyFGXz3yz5sCjjle12yF3B6z9854S8H0aV3Mxo0gN9+gx++3s4XLy5n5NS9WBdsVu650glwbtpHDL5gGd1eHgT16kUOasUKvjnjcR6ZcTJf0o8QTolga5ZzVf4YrvvfH2h53EER77pj1Ld8cMWnTPttHwJkcDqfcvIx20n7cDjsvXdVL5WJk0CpsnDCb8wcs5aZP5awbHkaBRvrsXJbI1aWtKCUrHLrpxOgVcY68nM3sG/DLeQ32U7T+iVkZyk5OZCd7ZxycoXs3DSyctLIzEknIzudjJyMXafcTDJyM5D0dBo3CNJ2H/eflIp/L4ZfD7/cqhW0rL7/p4hMU9Xue/r6VHishLYR1bUPgDNaYFFRpDymc3KedOfpl5VZbN6WjoaUUAhCQUXV6TEWCjmrhYK663rZeiUBAjtK2L4lyI6tQbZvU7Zvh+2bA/y2IYMVvzdk2dZmLChtT4h0hBB/yJzCJT0WcvGt+TQ8vbd/p1U28bdqFT8Nepm7RxzJF6G+hEgnP2sNhzT7jQPzd9A2P0CjxkLDxmk0bJpBgyYZpGemIWlCWhrOebrsPC9bRloaB7crJju7wvOlp0c1OHUs24faiqZ9qai6NuKzz+D002HCifdx3ID9nc/gpEnQrRu0b88djzdnyOeHcu1Zq7mg2yKyGueS2SiX9K2bnIa8Xj1k+zanzCM3BykshLQ0JDdn10DV2dlQUuI0Hrm5uz7nZe1OWWNS1sCkpe06lbXp4euVCV+n7FRReDsXfr3iMWWkxwh/7soeL3y9yr6LKq4f3u5WVJMYot2Gyu4bvjz8vpHuHwzuen/K1gl//UOhXeuUzR1cdnt6unNeVOTcnpPj7A+lpc5xZEmJs6/Ur+8s277dmf66tNQZ6bZpU+cxf/uNHQ334uOJzXnkg/Y80PJp7lz9t3LfG9PfmscRl3WiZ/MF3NXjKxo2zyazXT7pn37M+MKjuHX+VdzLPdxzVxD+/e/dX/+ZM3m/6785n/f59lv4wx92X2X3t8wfbUQ82gcA5sxh9cogv61Np6RYKS0OsWVTiF8WFPPl+Bw+K+jKXfmv8e8lF0NWVtWPFSO/f/E9/3fBZj7Yegr1ZAfH772QLm230rpdOq075NCosZCele6cstNJz0gjIwOE2PyWVGIzo06sftqWe5yKn/ddVyJeDF9HQ5UEVNljVtK2KRHanWjuX2F5MCTsKBQKlpYweWKQd2YfQvO0jUxb3ITGbZtGvm8Nbfx2Lk9fv4QnZ/RmGw3o12I6x3TazL7tsmjWrhFN98omJ1tJT3e+5vZuHmDvFkHne61jx2ofv8r2obKsRyJPwHnAy2HXLwP+U2GdOUB+2PUlQHPgP8ClYctfAc6r7jmry6p++GFlv1h2nbIo0k7M0f9r+r6+3vct/XXEDK3uL7FgIKTz3pmhw/u9pG8f9ZSOOvFh3fzIC6orV1Z5v51CIdVRo3TzdYN1Yd9Buvz/7lf99NNqn9d58qDq/PmqI0aoLl0a3X2MJ4JB1TUFJTrts1U64sHZ+twlE/WuY8bqgP3G6UlNf9SDs5doQ9lS7T5a3ek83qv5nSr7K7ICYlu5kdA2Iqp/XQ48sEav2x/5tNbvV8VTXtoG7Vr/Zz1j3x/1nt7j9Ys7J+iGHxbZZ9vsbvNmXf7sCH2+1zt68V5j9IisWdqQzbXa/5bQbveFTZtGFU4s24fanqJpXyqeqmsjvvxStU/XjbqjRZtdr01Ozs7LxWTqrTwc8zbBTnaqzemCtGEaGvlFxH36tV5DNY91Ee93EqM1cPVfnIOXSNau1U000mb1dugbb1T50dnJL21EPNoHVVVt3lxv4ZGIr2drWa63df5cSwt+i+7FiqWSEp12/2c6qMMX2inzZ82k2PP90k7xO9Vjm56/77e64IM5cdmdNoyfpXd0+0IPzFxSZRx38m/nQvv2UT1uVe2DXyo3/gScoqp/dq9fBvRQ1b+FrTPXXafAvb4E6AHcD0xW1bfc5a8AI1X1gwjPczVwtXv1IODn+G1VlZoDfunU5qdYwF/x+CkW8Fc80cSyn6q2iMWTJaKN8FH7EAt+2ldiKVW3C+retsWsfaitaNoXd3ks2ohUfp8rqkvbCra9seaLNiIB7YNf9xuLq2YsrpqpbVyVtg/xmTi55gqA1mHX84FVlaxT4JacNwY2RnlfAFT1ReDFGMW8x0Rkqvqg1A78FQv4Kx4/xQL+iseDWOLeRvilfYgFP+0rsZSq2wW2bR5LWBuRBK9FzNSlbQXb3hQW1/bBr6+jxVUzFlfNxDMuv3TE/hE4QETaiUgWzmCAIyqsMwIY4F4+D/jaLUsZAVzozpTQDjgAmJKguI0xiWFthDEmXqJpX4wxdZO1D8YkEV9UbqhqQET+CozCGZV4qKrOFZH7cfrUjMDpJ/+miCzG+Tf2Qve+c0XkPWAeEAAGqWrQkw0xxsSFtRHGmHiprH3xOCxjjA9Y+2BMcvFFcgNAVUcCIyss+2fY5SLgT5Xc9wHggbgGGFt+Kn33Uyzgr3j8FAv4K56Ex1LH2oja8tO+Ekupul1g2+apSO1LnPj+tYihurStYNubsuLcPvj1dbS4asbiqpm4xeWLAUWNMcYYY4wxxhhj9pRfxtwwxhhjjDHGGGOM2SOW3EgQEfmTiMwVkZCIdK9w2x0islhEfhaRUzyI7V4RWSkiM9zTqR7E0M/d/sUiMjjRzx8hnmUiMtt9PaZ68PxDRWStiMwJW9ZMRMaIyCL3vKmHsXi+z5jKpeL747c2Ipa8bm9iyU9tl1eq21fdwY2Hubf/ICJtEx9l7ESxvceLyHQRCYjIeV7EGEtRbO/NIjJPRGaJyFgR2c+LOGMhim29NqztmiginbyI049q0w7E83dBbfZfEQmGHVfEdFDVKOK6QkTWhT3/n8NuG+B+vywSkQEV7xvnuJ4Mi2mhiGwKuy2er9du37UVbhcRecaNe5aIHB52Wzxfr+riusSNZ5aITBKRw8Jui82xkKraKQEnoCPOvNfjge5hyzsBM4FsoB2wBEhPcGz3Ard4+Nqku9u9P5Dlvh6dPH6/lgHNPXz+44HDgTlhyx4BBruXBwMPexiLp/uMnap9z1Lq/fFjGxHj7fO0vYnxtvim7fJo+6vdV4HrgBfcyxcCw7yOO87b2xboArwBnOd1zAnY3j5APffyX5L1/Y1yWxuFXT4D+NLruP1wqk07QBx/F9R2/wW2efh6XQH8J8J9mwFL3fOm7uWmiYqrwvp/wxlwNq6vl/vYu33XVrj9VOALQICjgR/i/XpFGdexZc8H9C+Ly72+jBgcC1nlRoKo6nxV/TnCTWcC76pqsar+AiwGeiQ2Os/1ABar6lJVLQHexXld6ixVnYAz40e4M4HX3cuvA2d5GIsxiWRtRJLwU9vlkWj21fDXYzhwoohIAmOMpWq3V1WXqeosIORFgDEWzfaOU9Ud7tXvgfwExxgr0WzrlrCr9QEbyM9Rm3Ygnr8L/Lr/1uY7/hRgjKpuVNXfgTFAP4/iugh4J0bPXaUojs3PBN5Qx/dAExFpRXxfr2rjUtVJ7vNCnPYvS254b19gRdj1AndZov3VLREa6kHJsF9eg3AKjBaRaSJytcexlNlLVVcDuOctPY7Hy33GVC+V3h8/thGx5Mf2Jpb81nbFUzT76s51VDUAbAbyEhJd7KX6Z7Oimm7vQJx/T5NRVNsqIoNEZAlOhdb1CYrN72rTDsTzM1Xb/TdHRKaKyPciEsskdbRxnese1wwXkdY1vG8848LtvtMO+Dpscbxer2hUFruf2uyK+1dMjoUsuRFDIvKViMyJcKoqyxfp35qYZ76rie15oD3QFVgNPB7r568uvAjLvM7+91TVw3FKpgaJyPEex+M3Xu8zdZ7PP9Ox5sc2IpasvUkd0eyrqbQ/p9K2RCPq7RWRS4HuwKNxjSh+otpWVX1WVdsDtwP/iHtUyaE27UA8P1O13X/bqGp34GLgKRFpn8C4PgXaqmoX4Ct2Vb344vXC6Vo0XFWDYcvi9XpFw4v9K2oi0gcnuXF72OKYHAtlxCA+41LVk/bgbgVA67Dr+cCq2ES0S7SxichLwGexfv5qJOQ1qAlVXeWerxWRj3BK0yZ4GROwRkRaqepqt7RsrVeBqOqassse7TN1ns8/07HmuzYilnza3sSSb9quBIhmXy1bp0BEMoDGJG/Xv5T+bEYQ1faKyEnAXUAvVS1OUGyxVtP39l2cxLqpXTsQz89UrfbfsO+qpSIyHuiGMyZF3ONS1Q1hV18CHg67b+8K9x0fg5iiiivMhcCg8AVxfL2iUVns8Xy9oiIiXYCXgf7h72usjoWscsN7I4ALxRk1uR1wADAlkQG4B5tlzgYijnAbRz8CB4hIOxHJwmkgYjqqcE2ISH0RaVh2GehL4l+TSEYAZaMaDwA+8SoQH+wzpgop+P74qo2IJR+3N7Hkm7YrAaLZV8Nfj/OAr9UdTS0JpexnsxLVbq+IdAP+C5yhqsmcyItmWw8Iu/pHYFEC4/Oz2rQD8fxdsMf7r4g0FZFs93JzoCcwL4FxhR/XnAHMdy+PAvq68TXF+Q4dlai43NgOwhmcc3LYsni+XtEYAVwujqOBzW630Hi+XtUSkTbAh8BlqrowbHnsjoVqOyKpnaIePfZsnGxZMbAGGBV22104mbyfcbJYiY7tTWA2MAvnw9DKgxhOBRa6r8NdHr9X++OMiDwTmOtFPDgDEq0GSt39ZiBOX8yxOAcPY4FmHsbi+T5jpyrfs5R7f/zURsR4uzxvb2K8Pb5puzx8DXbbV4H7cX4sAOQA7+MMFDgF2N/rmOO8vUe6+8J2YAMw1+uY47y9X7nHeTPc0wivY47jtj7ttlszgHHAIV7H7JdTbdoB4vi7YE/3X5xZLma731WzgYEJjushd1+b6e5rB4fd9yr3dVwMXJnIuNzr9wJDKtwv3q9XpO/aa4Fr3dsFeNaNezblZ+qM5+tVXVwvA7+H7V9T3eUxOxYS9wGNMcYYY4wxxhhjkpJ1SzHGGGOMMcYYY0xSs+SGMcYYY4wxxhhjkpolN4wxxhhjjDHGGJPULLlhjDHGGGOMMcaYpGbJDWOMMcYYY4wxxiQ1S24YY4wxxhhjjDEmqVlywyQdEWkjIqNFZL6IzBORtl7HZIzxnoj0EZEZYaciETnL67iMMf4hIo+IyFz3GOIZERGvYzLG+IOIPCwic9zTBV7HY2ouw+sAjNkDbwAPqOoYEWkAhLwOyBjjPVUdB3QFEJFmwGJgtKdBGWN8Q0SOBXoCXdxFE4FewHivYjLG+IOI/BE4HOc4Ihv4RkS+UNUt3kZmasIqN4wvuJnS68Ku3ysif4+wXicgQ1XHAKjqNlXdkcBQjTEJFm37UMF5wBfWPhiT+mrQRiiQA2Th/HjJBNYkJkpjjBdq0D50Ar5R1YCqbgdmAv0SFaeJDUtuGL94Fwgv/zofeD/CegcCm0TkQxH5SUQeFZH0hERojPFKtO1DuAuBd+IWkTHGT6JqI1R1MjAOWO2eRqnq/IREaIzxSrTHEDOB/iJST0SaA32A1gmIz8SQdUsxvqCqP4lISxHZB2gB/K6qyyOsmgEcB3QDlgPDgCuAVxIVqzEmsWrQPgAgIq2AQ4FRiYrRGOOdaNsIEekAdATy3UVjROR4VZ2QwHCNMQkUbfugqqNF5EhgErAOmAwEEhutqS1Lbhg/GY5TSr43TpY1kgLgJ1VdCiAiHwNHY8kNY1JdNO1DmfOBj1S1NO5RGWP8Ipo24mzge1XdBiAiX+AcQ1hyw5jUFtUxhKo+ADwAICL/AxYlJDoTM9YtxfjJuzil5OfhNEKR/Ag0FZEW7vUTgHkJiM0Y461o2ocyF2FdUoypa6JpI5YDvUQkQ0QycQYTtW4pxqS+atsHEUkXkTz3checgYdtUPIkY5UbxjdUda6INARWqurqStYJisgtwFh3+rZpwEuJjNMYk3jRtA8A7tTQrYFvEhSaMcYHomwjhuP8KTIbZ3DRL1X100TFaIzxRpTtQybwrTs79BbgUlW1bilJRlTV6xiMMcYYY4wxxhhj9ph1SzHGGGOMMcYYY0xSs24pxpdE5FDgzQqLi1X1KC/iMcb4h7UPxpiqWBthjKmMtQ+pzbqlGGOMMcYYY4wxJqlZtxRjjDHGGGOMMcYkNUtuGGOMMcYYY4wxJqlZcsMYY4wxxhhjjDFJzZIbxhhjjDHGGGOMSWqW3DDGGGOMMcYYY0xSs+SGMcYYY4wxxhhjkpolN4wxxhhjjDHGGJPULLlhjDHGGGOMMcaYpGbJDWOMMcYYY4wxxiQ1S24YY4wxxhhjjDEmqWV4HYBXmjdvrm3btvU6DGNS1rRp09araguv49gT1j4YE1/J3D6AtRHGxFsytxHWPhgTX1W1D3U2udG2bVumTp3qdRjGpCwR+dXrGPaUtQ/GxFcs2gcRGQqcBqxV1c7usmbAMKAtsAw4X1V/FxEBngZOBXYAV6jqdPc+A4B/uA/7b1V9vbrntjbCmPiyYwhjTGWqah981y1FRFqLyDgRmS8ic0XkBnf5vSKyUkRmuKdTw+5zh4gsFpGfReQU76I3xhhjTIK8BvSrsGwwMFZVDwDGutcB+gMHuKergedhZzLkHuAooAdwj4g0jXvkxhhjjIk5P1ZuBIC/q+p0EWkITBORMe5tT6rqY+Eri0gn4ELgEGAf4CsROVBVgwmN2hhjjDEJo6oTRKRthcVnAr3dy68D44Hb3eVvqKoC34tIExFp5a47RlU3ArjHG/2Ad+IcvjHGGGNizHeVG6q6uqxUVFW3AvOBfau4y5nAu6parKq/AItx/n0xxhhjTN2yl6quBud4AmjpLt8XWBG2XoG7rLLlxpgU5SY3h4vIArdS/BgRaSYiY0RkkXve1F1XROQZt0J8logc7nX8xpjK+bFyYyf3H5luwA9AT+CvInI5MBWnuuN3nIOQ78PuVumBiYhcjVOOSps2bXa7vbS0lIKCAoqKimK3ET6Uk5NDfn4+mZmZXodijDF1kn3fJJxEWKZVLN/9AewYAvDVe2rMnnoa+FJVzxORLKAecCdOl7YhIjIYp0vb7ZTv0nYUTpe2o2r6hNY+GJMYvk1uiEgD4APgRlXdIiLPA//COej4F/A4cBU1ODBR1ReBFwG6d+++2zoFBQU0bNiQtm3b4ow9lnpUlQ0bNlBQUEC7du28DscYY+ok+76JmzUi0kpVV7vdTta6ywuA1mHr5QOr3OW9KywfH+mB7RjCjiFM8hORRsDxwBUAqloClIhIjbq0lVWIRcvaB2MSw3fdUgBEJBMnsfG2qn4IoKprVDWoqiHgJXZ1PansgKXGioqKyMvLS9lGB0BEyMvLS/nMsanEpElw001eR2FMwiyfvZlbDxnJNXt/zHPdhxLatMXrkAD7vomjEcAA9/IA4JOw5Ze7JeZHA5vdHyejgL4i0tQtQ+/rLqsxe09Nqvv4Y3j4Ya+jqLX9gXXAqyLyk4i8LCL1qXmXtnJE5GoRmSoiU9etW7fbk1r7YOqCHTvgkkvgt9+8i8F3yQ13urZXgPmq+kTY8lZhq50NzHEvjwAuFJFsEWmHUzY2pRbPv6d3TRp1YRtNJUaMgKeegkDA60iMibs3n1jHIV0zeGbeiXy4sTeDpl3FSQetYP26iMV9CVcX2uJ4bqOIvANMBg4SkQIRGQgMAU4WkUXAye51gJHAUpxxuV4CrgNwBxL9F/Cje7q/bHDRPYxpT++aNOrCNprI3n8fXnjB6yhqLQM4HHheVbsB29k1q1IkUVWIq+qLqtpdVbu3aNEi8gPVgc9OXdhGU7n58+F//4PJk72LwY/dUnoClwGzRWSGu+xO4CIR6YrToCwDrgFQ1bki8h4wD2emlUE2U4oxldixwznfvh0aN/Y2FmPi6H9Pr+Pyv7egV/pEXnsng/0uOJpXz/2M6z46iYv6rObLmfuQnu51lKY2VPWiSm46McK6Cgyq5HGGAkNjGJoxKSkYdE5JrgAoUNUf3OvDcZIbNe3SZoypoKx9CIW8i8F3lRuqOlFVRVW7qGpX9zRSVS9T1UPd5WeE93VT1QdUtb2qHqSqX3gZf21s2rSJ5557rsb3O/XUU9m0aVMcIjIpJzy5YUyK+ubd1Qy4sQm9Miby5bf1aXvh0YjAVe/359n9n+Crufvw7/uS/wi9Nuz7JvXYe2riLRRK/sJPVf0NWCEiB7mLTsT5g7SmXdqSirUPJhHKkhteJkF9l9yoyypreILV7CEjR46kSZMm8QrLpBJLbpgUt276Ci6+VGif9guffNWAnGO67boxPZ2rnj6MS3mTfz0gzJ3rXZxes++b1GPvqYm30PSfCG7Z5nUYsfA34G0RmQV0BR6khl3ako21DyYR/FC54cduKf5w440wY0b169VE167OeAeVGDx4MEuWLKFr165kZmbSoEEDWrVqxYwZM5g3bx5nnXUWK1asoKioiBtuuIGrr74agLZt2zJ16lS2bdtG//79+cMf/sCkSZPYd999+eSTT8jNzY3tdpjklcLJDREZCpwGrFXVzu6yR4HTgRJgCXClqtpfEClKi4oZeMJSNgSP4vP//U7jXl13W0f+eCpPHnw8ny86gxtuaMSYMYLnXYTt+yb12HtqUlBw/e8ESjz81RIjqjoD6B7hphp1adtj1j6YFFVW2WWVGwaAIUOG0L59e2bMmMGjjz7KlClTeOCBB5g3bx4AQ4cOZdq0aUydOpVnnnmGDRs27PYYixYtYtCgQcydO5cmTZrwwQcfJHozjJ+lcHIDeA3oV2HZGKCzqnYBFgJ3JDookzgfXjCMTzf34sErF9P1oo6RVxKh+V3X8K/gnYwdK3z0UWJj9Av7vkk99p6aeAupEAjZT4dkZO2DSQSr3PCzKrKfidKjR49y80Q/88wzfOQeia9YsYJFixaRl5dX7j7t2rWja1fn38ojjjiCZcuWJSxekwRSOLmhqhNEpG2FZaPDrn4PnJfImEzibP1hHjeMOIHD8lZw/Yudq175ggu45s5/8t8Ni7n55g706wf16iUmzojs+yb12HtqUlBQ0wiqJTdqzdoHk6JszA1Tpfr16++8PH78eL766ismT57MzJkz6datW8R5pLOzs3deTk9PJ5DsIz+Z2Erh5EYUrgIqHXC4ujnqjb899uf5rCSf599uTEZ1afvMTDJuuZFndvyZX3+FRx9NSIi+Zt83qcfeUxNrIRUCatNMpQJrH0w8lO0SNluKAaBhw4Zs3bo14m2bN2+madOm1KtXjwULFvD9998nODqTEupockNE7sKZKvrtytaJZo5640+/T1vKU3NO4pwDZnPMKY2iu9PAgfTOm8P5LcczZIiyfHl8Y/Qb+75JPfaemngLhtIIYsmNZGTtg0kEP1RuWLcUH8nLy6Nnz5507tyZ3Nxc9tprr5239evXjxdeeIEuXbpw0EEHcfTRR3sYqfGlKVMgJwe6dKl8nTqY3BCRATgDjZ7oDgxmUsxT18xnC3/kn8+3iv5O9evDvffyyN8GMCJzCXfemcFbb8UvRr+x75vUY++piTencsN+OiQjax9MIvhhzA2pq8f63bt316lTp5ZbNn/+fDp2rGQQuhRTl7a1zjjmGGjRAkaMqHyd5s1hwwZ4/HG4+ea4hiMi01Q10mjk8XzOtsBnYbOl9AOeAHqpatR9TSK1D8aftq3ZTv7epZzQZjEf/lrD3S0YhB49uOvny3lw+w388AP06BGfOCuqS21wpG31on2IJTuGqDvbanY5IXcy44qOIRiEtDjXfidzG2HtQ93ZVlPeBx/AeefBs8/CdXGcNLmq9sG6pRiTKoqKoLi46nVSuHJDRN4BJgMHiUiBiAwE/gM0BMaIyAwRecHTIE3MvXXbLDbThFsGZ9b8zunp8MILDN5+N3vX28xNN0EdzfcbY0y1QurMm21DLRhjIrExN4wxsRMMVt3JLRSCwkLncgomN1T1IlVtpaqZqpqvqq+oagdVba2qXd3TtV7HaWJHFZ4d3pKu2fM45poqumNV5cgjafiXS/l34S1MmgTvvx/bGI0xJlWUzZRiyQ1jTCR+6JZiyQ1jUkUgUHVrEj7ydQomN0yfnDgVAAAgAElEQVTd8+1by5izoz1/PWM5kiZ7/kAPPsgVzT+ja/2F3HabVlsAZYwxdVEIp531crBAY4x/+WFAUUtuGJMqqqvcKOuSApbcMCnhxYc20JhNXPTI4bV7oCZNSL/3bh7dfh2//iq8/HJs4jPGmFRilRvGmKpY5YYxJnaiSG4oECTNkhsm6W1eV8KH8ztyUbsfqNe2Ze0fcOBATsxfyPENf+LBB3VnDy5jjDGOkJvcsMoNY0wkZYlPq9wwxtReMFh1qnTHDi5gGAN43ZIbJum9/89ZFFKPK69vGJsHzM5G7rqT+7feyKpVwosvxuZhjTEmVdiAosaYqljlhiln06ZNPPfcc3t036eeeood4d0OTN0TCFRbubGYDnzOHwlts33FJLfX3smmY8ZCjvzrUbF70CuvpNfeCzmh2U889FD5nlypxr5vUo+9pybegli3lGRl7YNJBBtzw5RjDY+plSgqN4rIYRNNmbc+BmX8xnhk4Udz+W7zoVzRdzWSkR67B87Ohr/+lfs2/o01a+D552P30H5j3zepx95TE29llRvWLSX5WPtgEsEPlRsZ3j21v914I8yYEdvH7NoVnnqq8tsHDx7MkiVL6Nq1KyeffDItW7bkvffeo7i4mLPPPpv77ruP7du3c/7551NQUEAwGOTuu+9mzZo1rFq1ij59+tC8eXPGjRsX28BNcohizI1C9gVg4sZOdE5QWMbE2uv/WEgaB3PZE91i/+DXXssfHnyQvk3mMGRIZ665Bho0iP3ThLPvm9Rj76lJRUGcZLJVbtSOtQ8mJRUWEnjlPWCApwlQS274yJAhQ5gzZw4zZsxg9OjRDB8+nClTpqCqnHHGGUyYMIF169axzz778PnnnwOwefNmGjduzBNPPMG4ceNo3ry5x1thPBNFcqOIHAC+23Io1yYoLGNiKbhkGW/M606/dgtpdVDH2D9BXh4MHMh9z13LMcGJPPss3H577J/Ga/Z9k3rsPTXxtrNyoyQIxLBqzsSdtQ8m7pYsIThzNmCVG75UVfYzEUaPHs3o0aPp1s35Z3Lbtm0sWrSI4447jltuuYXbb7+d0047jeOOO87bQI1/BAJRdUsB+K6wllNnGuORr28ZSQHX8cRtG+L3JLfdxtH/bU+/feby6KOHMGhQfKs37Psm9dh7alLRzsqNYktu1Ia1DyYlBYM72whLbpjdqCp33HEH11xzzW63TZs2jZEjR3LHHXfQt29f/vnPf3oQofGdKCs36qUX8UtwP1atgn32SVx4xtTaxo28+mlzmmZt44wr8+L3PPn5MHAg9754NUcHv+M//4HBg+P3dF6z75vUY++piYeds6UUBYAsb4Mxe8zaBxMXwSABN7VgA4oaABo2bMjWrVsBOOWUUxg6dCjbtm0DYOXKlaxdu5ZVq1ZRr149Lr30Um655RamT5++231NHVXNgKK6fQdF5NJj7+UAzJnlYVrVmD2w6YmhfBQ8nYvPKSY7O85PNngwR2VOp3+LqTz6qJJqzat936Qee09NvJX9KxssTu5BN0RkmYjMFpEZIjLVXdZMRMaIyCL3vKm7XETkGRFZLCKzRCQpS1+tfTBxZ5UbpqK8vDx69uxJ586d6d+/PxdffDHHHHMMAA0aNOCtt95i8eLF3HrrraSlpZGZmcnz7nD+V199Nf3796dVq1Y22E9dVc1UsMVbigHIaxSAlVC8tQTcbirG+F4gwHv/WUsRuVzx99z4P1+bNvDQQ9x703UcxRT+8x+44474P22i2PdN6rH31MSVKqGyqWCLU2K6lD6quj7s+mBgrKoOEZHB7vXbgf7AAe7pKOB59zypWPtg4i4sueFl5YaoqnfP7qHu3bvr1KlTyy2bP38+HTvGYYA6H6pL21pnpKfDfvvB0qURb95024M0ffROrjhqPq/90JEPhm7mnCsbxy0cEZmmqt3j9gRxFKl9MB77+GOOPbslW9p0ZvayRogk4DlDIejTh9Mm3s7keifyy8psGjWKzUPXpTY40rYmc/sAdgxRl7bVuEIhWqevpIDWTB6xjqNPbxHXp4tnGyEiy4Du4ckNEfkZ6K2qq0WkFTBeVQ8Skf+6l9+puF5lj2/tQ93ZVhNm0iT+0fNrHuAf3HQTPPFE/J6qqvbBuqUYkwpUnR9iVdSBFW4pBaBRQyehWbqtOCGhGRMLCx//lMkcyxWD6icmsQGQlgYffMB9h37A79syueeECVBYmKAnN3tKRG4SkbkiMkdE3hGRHBFpJyI/uCXnw0Qky103272+2L29rbfRG+NTwWAqVW4oMFpEponI1e6yvcoSFu55S3f5vsCKsPsWuMvKEZGrRWSqiExdt25dHEM3xqdszI3IRKS1iIwTkfnuwckN7vKU7gtnTK2UJTWqaE2Ktjl9ZBu6/zxbcsMkjaVLeXtiG0SUiy9N8Aj9zZtzxPfPcm2nb3lmWk+mHjIAVq5MbAwmaiKyL3A9zr+ynXGmdLgQeBh4UlUPAH4HBrp3GQj8rqodgCfd9YwxFYVCu0rOS5I+udFTVQ/H6XIySESOr2LdSOn03creVfVFVe2uqt1btIhvVYsxvuSTMTd8l9wAAsDfVbUjcDROo9OJXX3hDgDGutehfF+4q3H6wu2xutBNpy5sY50TcAf3qqI12ZXccD72pTtK4h6WMbGgr7/B21zCCT2LvZnhJyeHhyb1Yq9mpVz56z1sP64fFBTU+mHrQlvs0TZmALkikgHUA1YDJwDD3dtfB85yL5/pXse9/USRPa8NsvfUpKwUqtxQ1VXu+VrgI6AHsMbtjoJ7vtZdvQBoHXb3fGDVHj7vnoacNOrCNppK+GTMDd8lN1R1tapOdy9vBebjlH+FH4BUPDB5Qx3fA03KGqeaysnJYcOGDSn9wVRVNmzYQE6ODSSZUspakSpak8Idzn7dsInT8JRuL417WIkmIkNFZK2IzAlbFrHqyyQJVaa8NJMldOCSq7xrtxo3htfeyWEenRi44l703POgdM8/Q/Z9E7fnXAk8BizHSWpsBqYBm1S1bIqH8LLynSXn7u2bgT2aZ9jeU5PSQqFdyY2S5J1tTUTqi0jDsstAX2AOMAIY4K42APjEvTwCuNytFD8a2FzVeBuVsfbBpDyfVG74erYUt+9rN+AHKvSFE5Hq+sLt1vC4/equBmjTps1uz5efn09BQQGp3lcuJyeH/Px8r8MwsRRFcqNou3Nbo6ZOwxMoTMnKjdeA/wBvhC2rbAR0kwy++463V/chOzPIOeckuEtKBX37woMPCoMHn0v3KZO55a674JFH9uix7PsmPtzk5ZlAO2AT8D5OhWdFZb8woio5dx/bjiGwY4g6K/xf2eTulrIX8JFboJUB/E9VvxSRH4H3RGQgTnL0T+76I4FTgcXADuDKPXlSax9MyguFfDHmhm+TGyLSAPgAuFFVt1RRJRr1gYmqvgi8CM5IxhVvz8zMpF27dnsWsDFeKmtFquqWssO5rWGzTABKdyT3PPWRqOqECAMCngn0di+/DozHkhtJI/D62wzjPk7/o9I4fpP7RO2222DaNLh9+CN0fbQvJ/X5AvpH+u1cNfu+iZuTgF9UdR2AiHwIHItT1ZnhVmeEl5WXlZwXuN1YGgMbIz2wHUOYOi28ciOJu6Wo6lLgsAjLNwAnRliuwKDaPq+1Dybl+aRyw3fdUgBEJBMnsfG2qn7oLo57XzhjklbZmBtVdUtxJ3lomJcFQGlh6nVLqURlI6Abvysp4at317OWllwywB+5eBEYOhQ6dYIL0ofzyyX/gFX2leMjy4GjRaSeO3bGicA8YBxwnrtOxZLzslL084CvNZXrxo3ZU+GVG6XJ2y3FGBMnltyIzD0YeQWYr6rhM+TGtS+cMUktmm4pRc7xeqMW2QCUFibvPy/xYNO4+dDYsby97Qya1C/Zk+KIuGnQAD76OI1Q/Yacs2koO676qzMds/Gcqv6AMzDodGA2znHOizjVWjeLyGKcMTVece/yCpDnLr+ZXYOVG2PClRtzw44fjDEV2FSwleoJXAacICIz3NOpwBDgZBFZBJzsXgenL9xSnL5wLwHXeRCzMd6KpltKkdODq2HLXABKC1OvW0olKqv6KsemcfOf7W9/zEeczZ8uSCc72+toyuvQAd4Zls5MunDLqJNg2DCvQzIuVb1HVQ9W1c6qepmqFqvqUlXtoaodVPVPqlrsrlvkXu/g3r7U6/iN8aWwf2UDxVa5YYypwCeVG/6o8w2jqhOJPI4GxLEvnDFJLZpuKW5yo16TLNIIUlpUZ/55Kav6GkL5qi/jZ8XFfPlRIdtpwEWXeh1MZP36wY3XK08+fR0XXHsmvU49FRo18josY4yJvbDKjWDAkhvGmApCIZsK1hgTI9FUbpQ4H/ecXCGTUkpLUq+MXkTeASYDB4lIgTvqeWVVX8bPxozh4x0nk9eohOOO8zqYyv37wTT237eIP29+jOKnnvc6HGOMiY/wyo0kngrWGBMnPqncsOSGMSkgUBzkHD5geqBLpeuUJTdyc3GSG4FKZyBKWqp6kaq2UtVMVc1X1VdUdYOqnqiqB7jnEWdCMP5S+u4HfCanc/pZ6WT4rsZwl3r14IVXc1jMATw1pAi2bvU6JGOMiTkNhtCyyg1LbhhjKrIxN4wxsbJ+PXzEOXynx0Qe2DAQoDDoTAGbkwOZEkjJ5IZJEUVFTPhoA5u0CWedk+51NNU6+WQ4s9fv/LvwZlY/9JrX4RhjTMyFSnf9WgmUpl7lpzGmlqxywxgTK2VzzpeSGTm5UVhIETmkp4XIyChLbiQ4SGOiNWoUH+84mdzsICef7HUw0Xns5aaUSDZ3P9Pc278sjDEmDkJh42zYVLDGmN2ETxdtlRvGmNoo6/8aICNyi1JaShE55GY6GQ0nuWEff+NPOuw9Pk47h1NOEerV8zqa6HToAH85dTmvbj+fBS9O8DocY4yJqfCEhlVuGGN2Y5UbxphYKRscNEBG5BYlEKCIHHIyncRHpgQoDVq3FONDJSVMH1FAQWhfzjw7ub6i7vzvfuRKEXffn+l1KMYYE1PhlRuW3DDG7CYUsjE3jDGxUVa5UUpm5BYlGKSQ3J3JjQwJWuWG8advv+Xj7SeRlqacdprXwdRMy30zufkPPzL8tz8w88vVXodjjDExU65bSsCSG8aYCqxywxgTK2VjblTaLcWt3Mgtq9xIC1rlhvGnTz/lYzmb444N0by518HU3E3/b38asJUht23wOhRjjIkZ65ZijKmSJTeMMbFSdqBRabeUYNDplpIV3i3FPv7GZ1RZ8uFM5mhnzjrX/7OkRNL0sDZct9/nvDe7I4sX2qB7xpjUYJUbxpgq2VSwxphYqbZbSiDgdEvJctZzKjfs4298Zv58Rq7oDMDpp3scSy3cdGsmmZTy+K3WNcUYkxrCExpWuWGM2Y1VbhhjYqXa2VLKBhTdmdwIURpMzn/GTQr78ktG05cObUtp397rYPbc3ledysWZw3lzZB5btngdjTHG1F6odNexRcCmkjfGVBQK2VSwxpjYqHa2FLdbSm52WOVGyD7+xl9Kxn7LODmBvqcm+Wwjublce1oB2wM5vP1yodfRGGNMrYVXbli3FGPMbqxywxgTK+XG3IimW0p60Co3jL8EAkweV8R2rU/fvl4HU3tH3n4C3ZjO80/sQO13gDEmyZWbCtYqN4wxFdmYG8aYWClLbpSSGTldurNbirNeZlqI0pAlN4yPTJ/O6MI/kJ4Wok8fr4OpPelxJNe2GsHslXlMmeJ1NMYYUzvlKjc8/OFijPEpq9wwxsRKtWNulHVLyXGTG+mW3DA+M24co+nLMUcGaNTI62BiQITz/5JHFsW88+xGr6MxxphaSbXKDRFJF5GfROQz93o7EflBRBaJyDARyXKXZ7vXF7u3t/UybmN8Kyy5YZUbxphaKTvQqLZbSrYlN4w/bRnzA9M5nD59s7wOJWaaXH0+p/IFwz7MsH86jTFJrdxsKSmQ3ABuAOaHXX8YeFJVDwB+Bwa6ywcCv6tqB+BJdz1jTEVhA4pa5YYxplai7pYSXrmhltwwPhEMMuk7JUQ6vXp5HUwM7bUXFx2xkN+2N+Kbsanxa8AYUzeFV24ke7JWRPKBPwIvu9cFOAEY7q7yOnCWe/lM9zru7Se66xtjwoWNuREKeTfYmCU3jEkBpdUNKLqzW4pz1ancyEhghMZUYcECJhQdSUZ6iKOP9jqY2Drttk40YCvvPL7S61CMMWaPlavcSPLkBvAUcBtQlrHJAzapalkWugDY1728L7ACwL19s7t+OSJytYhMFZGp69ati2fsxvhTeLcUD2dUsuSGMSkgUM1UsMHiACVk76rcyFCr3DD+MWUKEzieIzoXU7++18HEVr2zT+G07K/4ZHzjpP+30xhTd6VK5YaInAasVdVp4YsjrKpR3LZrgeqLqtpdVbu3aNEiBpEak2TCBxQNWnLDGFMLZf1fS8mMeNRRXOQ0Mjk5znd0ZkaIUq07lRsicpOIzBWROSLyjojkeB2T2aVw0k/8yJEcf3IKvi2ZmZzVdwfrSprw/Yi1XkdjjDF7JPzHSiCY1L0yegJniMgy4F2c7ihPAU1EpOzAKB9Y5V4uAFoDuLc3BmyUaGMqCp8K1sOeuJbcMCYFBAJVd0spKnRuz811rmema51JbojIvsD1QHdV7QykAxd6G5UJN+WbQkrI5rjjk/qAuVL9/3UsmZTw8aOLvA7FGGP2SLA0vHIjedtqVb1DVfNVtS3OscDXqnoJMA44z11tAPCJe3mEex339q9V1bu/pY3xq3IDilrlhjGmFgKl7nkl3VLKkhs5ZWNuZCqlmpmo8PwgA8h1/3Wpx65/ZIzXCgv5bsleAPTs6XEscdLosHb0yZvNJ1P2RktKvQ7HGGNqLIUqNypzO3CziCzGGVPjFXf5K0Ceu/xmYLBH8Rnjb+XG3PAuDEtuGJMCyio3KuuWUljonOfklnVLgVLqRuWGqq4EHgOWA6uBzao6uuJ6NhiYR2bM4IfQkRy4z1aaNfM6mPg587xMFgXbs+C/33gdijHG1Fj5AUVTI7mhquNV9TT38lJV7aGqHVT1T6pa7C4vcq93cG9f6m3UxvhU+JgbVrlhjKmN0lLnQKO6yo3cemXJDXUSIXWAiDTFmcqtHbAPUF9ELq24ng0G5g2d8iM/cBRHHZPaA9yedlsnAL588VePI0l9ItJERIaLyAIRmS8ix4hIMxEZIyKL3POm7roiIs+IyGIRmSUih3sdvzF+FF65EQylRnLDGBND4WNueDjosC+TGyIyVETWisicsGX3ishKEZnhnk4Nu+0O98DkZxE5xZuojfFO2YCilY65UeSc76zcyIQAmdSRXqMnAb+o6jpVLQU+BI71OCbjWvHdctawNz161/M6lLhqs38GBzdbw6i5+bDWBhaNs6eBL1X1YOAwYD5OKflYVT0AGMuu0vL+wAHu6Wrg+cSHa4z/lavcsOSGMaaicrOleBeGL5MbwGtAvwjLn1TVru5pJICIdMIZEOgQ9z7PiUhq/wVoTAXVzZZSWOQciOTUcz7ymW7RRqC0TmQ3lgNHi0g9ERHgRJwfO8YHpkx19smjjvI4kAQ45dQMvtHjKXz1Xa9DSVki0gg4Hre/vKqWqOomnOqt193VXgfOci+fCbyhju9xZkxoleCwjfE9q9wwxlQpbEBRq9yoQFUnEP00S2cC76pqsar+AiwGesQtOGN8qFzlRqRuKRUrNzLcMToKPRzxJ0FU9QdgODAdmI3T7r3oaVDGEQjww/K9yUoPcNhhXgcTf6dcnEcRuXz7ouXW4mh/YB3wqoj8JCIvi0h9YC9VXQ3gnrd0198XWBF2/wJ3mTEmTPkxNyL8fAgE4IQTYPz4xAVljPEPG3Njj/zV7RM7tKy/LDU4MLEBA02qirZbSm59t3Ijyx2jo7BuzNygqveo6sGq2llVLysbKMx47OefmRI8gm5tfycry+tg4q9XL8hKDzBqaQf45Revw0lVGcDhwPOq2g3YTtWzG0T6CzriUZkdQ5i6rNxsKRrh58PWrTBuHEydmsCojDF+oYEgoZ3JDe+qu5IpufE80B7oijPjwePu8qgPTGzAQJOqykYurzS5Uezcnl3PaXTKuqWUFnlYN2bqvOBPs5jGEfQ4qm6UONerB8cdVcJo+sLHH3sdTqoqAArcii1wqrYOB9aUdTdxz9eGrd867P75VDJVtB1DmLqsrHIjU0ojd0vZ2T+2bvxpYowpL7y6K7h7EXnCJE1yQ1XXqGpQVUPAS+zqehL1gYkxqarsWKKUzIjdUkpKnPPM7PJjbpTusIMQ452F41exnQYccUITr0NJmFPOqsccDmXlsIleh5KSVPU3YIWIHOQuOhGYB4wABrjLBgCfuJdHAJe7s6YcjTNV9OpExmxMMiir3MiSUgKhSrqlhJ8bY+qU8ORGhJ8iCZM0yY0KA3ydDZTNpDICuFBEskWkHc6I51MSHZ8xXqqucqMs+ZGZ41ZuuN1S6sKYG8a/fpri7JiH98jwOJLEOcWdz2v0lCZgXRvi5W/A2yIyC6fa80FgCHCyiCwCTnavA4wEluKM1/UScF3iwzXG/8p+uGSlBQhG6pZiyQ1j6rSy3yLg7aDDvjyiFJF3gN5AcxEpAO4BeotIV5wuJ8uAawBUda6IvIfzz0wAGKSqVmtv6pRyyY0I6dKKyY2MTDe5Yd1SjIemL2lMTnoJHTvWgQE3XIceCns3L2XU+pO5cuRIGDCg+juZGlHVGUD3CDedGGFdBQbFPShjklzZoUVWWsAqN4wxuylLgAohTys3fJncUNWLIix+pYr1HwAeiF9ExvhbdVPB7kxuZJcfUNSSG8YzGzfy044DObT1BjIy6s7MmyLQt38Gn73Vl+CYm0i35IYxJgmEAs6vlaz0IMGAJTeMMeXtrO6ihGAo07M4kqZbijGmctV2S3GPNTJznXxmWZLDuqUYr+i8+UzncA7vXPfGfTmln7BRmzFt9AZQ76ZLM8aYaJUdWmSlBQlo+u4rWHLDmDqtrI3IpNTG3DDG1E713VKc27NyK4y5Uexh62PqtF8nrmATTenWs57XoSTcSSc55+PWHQILF3objDHGRGHngKIZQQKhyMmNi3mbp6f2THBkxhg/KMtrOpUbNhWsMaYWSoNuJUa03VKscsN4bPqkIgAOP6mZx5EkXsuW0LF9Cd/QC8aO9TocY4yp1s7KjfQgwUg/HwIBvuU4flydn9jAjDG+UK5yI9KgwwliyQ1jUkBZ5UaQDDTC5NKlAef2silgdyY3rHLDeGTGvCzSCdC5S938Gjr+xEwmynEEvxrndSjGGFOtssqN7IxQpd1SSsksN2OCMabu2JkApQTwrtdt3TyqNCbFlJt+qbTy5Ea6ezxi3VKM12atas4BjdaQm+t1JN7o1VvYqg2Z8fVGG3fDGON7O/+VzdBKp4ItJZNSS24YUyeFDygKEQvJE8KSG8akgEBY37bSkt1/KJUGhExKkP/P3pmHt1Weeft+ztHq3VlJ4myQhJCFBJKyJEDDVpYu7B0obaGlH9Mp7XSZTlu+TqfTb9qZrkP3ha50o7QwBUopFMIetiQQshISkkB2x4l3Wbak835/nCNZlmXHsS1Lsp77unTpLO+RHh1Lr9/zO7/neb1m/pDn3NDZUpR80N7Oxo7jWTi1Od+R5I1zznGfn2o+GbZvz28wiqIoRyFVc8PvuPW9Mkk5N/TSQlFKkeSN1qS4ka+iotoDKcooIH0wEY/1JW50z0rhD7oWDnVuKPmgfd02dnA8CxeUrmNhyhQ4YWonT3EOPPdcvsNRFEXpl5Tl3GeyFy9POTf00kJRSpH0mhvp6yON9kCKMgpId270KW5Id/FQrbmh5JNNKw9gsFi4vCrfoeSVc84P8JS8FefZ5/MdiqIoSr84TrdzI4HdXak8STxOHF+P8YiiKKVDZs0NdW4oijJoYonu4l5Z01ISgp80cSOkzg0lf2x4IQLAwgsm5jmS/PLWFcIRM4bNjx/MdyiKoij9kvAs50G/59zIEDdMLE68CJwbIhISkRdF5BUR2SQiX/K2zxSRF0Rkm4jcJSIBb3vQW9/u7Z+Rz/gVpVBJiRvezVR1biiKMmh6ODeyzO4ai1v4JS0tJSludKm4oYw8G7YGKJd2Zp4YyHcoeSVZd+PJbZOgtTW/wSiKovRDd80N4zo3MgYb8U73SibuFPylRSdwnjFmEbAYuFhEzgC+BtxmjJkNNAI3ee1vAhqNMbOA27x2iqJkkKq5YbnXG+rcUBRl0KQPJrKmpSSsnmkpKXGjdGseKPljw/6xzK/Zi1Xi/4FmzICp4zt4ypwNq1fnOxxFUZQ+Sd2V7cO5kXSCpjtJCxHj0uat+r2HAc4D7va23wFc7i1f5q3j7T9fRDT3RlEySNXckESP9ZGmxIeWijI6SBc3MtNgISludPcy3WkpKm4oI0wsxobICSyY1pLvSPKOCJyzwuJJ3op54cV8h6MoitInybuwgUB250aseJwbiIgtIuuAeuAR4HWgyRiT/FB7gCne8hRgN4C3vxkYm+U1bxaRNSKy5tChQ7n+CIpScKQEUMv9GalzQ1GUQdPDuZEtLSVh4bfSnBthdxq3Xs6NZ58dlXeQRaRGRO4WkVdFZIuInJnvmEqVg8/v5BATWLgg35EUBm+9MMhBjmPbE3vzHYqiKEqfpMQNPzjYOJ0976TEk84Np7CdGwDGmIQxZjFQB5wGnJStmfeczaXR686QMeZ2Y8xSY8zS8ePHD1+wilIkJK9FkuKGOjcURRk06YOJY0pLyWz7mc/A5z+fmyDzy3eAh4wxc4FFwJY8x1OybHjULZ5Z6jOlJEnV3VhTlt9AFEVR+iF1VzbgjhsS0expKXFTPJcWxpgm4AngDKBGRHzerjpgn7e8B5gK4O2vBo6MbKSKUvik0lJsd0GdG4qiDJq4YyF4A4u+nBvpaSl9OTc6OiAazVmc+UBEqoBzgJ8DGPY+XBAAACAASURBVGO6vAGNkgc2ru4AYOHFU47SsjSYMwfGl0dYdeQkqK/PdziKoihZ6U5LcY0Mia6et2WT44lCT0sRkfEiUuMth4ELcG94PA5c7TW7AbjPW77fW8fb/5gxRnN6FSWDhDe5QUDFDUVRhkrc2IStTqCPmhuOhd/KJm5kNoxBZ2euwswXxwOHgF+KyMsi8jMRKc9spPmyI8OGrQEmWIeYMLPXn6AkEYFliyOsYjmsXZvvcBRFUbKS6dyIRzNrbiTTUnwUOJOAx0VkPbAaeMQY8wDwWeBTIrIdt6bGz732PwfGets/BXwuDzErSsHTXXNDC4oqijJE4o5FyHKViuzODTt7zY1MISQWg65MxaPo8QGnAj8yxpwCtJNlcKL5siPDhgPjWFizJ99hFBTLL65gO7Opf3xTvkNRFEXJSi/nRmZaStK5UeBpKcaY9caYU4wxJxtjFhhj/p+3fYcx5jRjzCxjzDXGmE5ve9Rbn+Xt35HfT6AohUlqKlifOjcURRkirnOjH3HDsXs4N3xhv7u9NMSNPcAeY8wL3vrduGKHMsI4cYdNkZksnK4zpaSz/LwQAM8+PupcU4qijBJSd2WD7gVMvLPnbdl4l+fcMAXv3FAUJQckPGHTb3t1edS5oSjKYIk7NiHbFSUGkpYiAT8+Yr0Lio5CccMYcwDYLSInepvOBzbnMaSSZceq/UQoZ4HOlNKDJUsgYMVYtWVMvkNRFEXJSsq5EXSfM8WN7oKihT9biqIow09KAFXnhqIoQyVmbMK2q2okbWE99js2fiutl/H58BMjFstoOwrFDY+PAb/zcmwXA/+V53hKEp0pJTvBICydfohV7Ytgr04JqyhK4ZFIiRvupUOiM6PmhpeWos4NRSlNuguKup2FOjcURRk0cWMT9h0lLcVO62Vs2xU3MtuOUnHDGLPOq6dxsjHmcmNMY75jKkU2rOlEcJh/UV2+Qyk4li8zrGUJ0WdfyncoiqIovXC8Cxd/Ki0lu7ihzg1FKU2SMyUF/G5foM4NRVEGTdzYhDznRiyezbnh6+ncAE/cKBnnhlIAbHgtwPHWLsqnj8t3KAXH8neNo4sga/96IN+hKIqi9CKRAMHBH0g6NzLSUryUWHVuKEppknR3+X1ac0NRlCHiihvuXZSszg2T4dwA/MRLKS1FKQA27PdmSpHeAlyps+xcN5F91XN6bhRFKTwcAxYOvqDrzMicCjbu1fCKo+KGopQiiaRzw+eqHOrcUBRl0MTxEfZ5NTeypqX48NsZzg1R54YyckSjsK2jjoXTmvMdSkEyfjzMrjrAqp1TwJijH6D0i4jYIvKyiDzgrc8UkRdEZJuI3CUiAW970Fvf7u2fkc+4FaVQSSQEWxzsgCduZDo3vLQUBztvFzWKouSPVFpKwF1XcSMNEfmFiNSLyMa0bWNE5BFvYPKIiNR620VEvusNTNaLiE7xqJQccWMT8rmqRixbQVFj90pL8Umip7hhTLe4oRdXyjCz5flmHGwWLtDvVl8sn9fEs7GlmF1v5DuU0cDHgS1p618DbjPGzAYagZu87TcBjcaYWcBtXjtFUTLIdG4kurKnpUD2myyKooxukgVFNS0lO78CLs7Y9jlgpTcwWemtA1wCzPYeNwM/GqEYFaUgcBz3Tkm3cyObuOHD78t0bsR7CiHpvZCOTJRhZsPKegAWLNOZUvpi+fkhGhjPtvu3HL2x0iciUge8HfiZty7AecDdXpM7gMu95cu8dbz953vtFUVJo5dzIyMtRcUNRSltUrOlBLSgaC+MMU8BRzI2pw9AMgcmvzYuzwM1IjJpZCJVlPyTHESE/f2kpRgffrvnHXO/JHpOG5s+MtHUFGWY2bA6SpAos8+bmu9QCpbl10wGYNXfWvIcSdHzbeAzQHJoNRZoMsYke8c9wBRveQqwG8Db3+y1VxQlDcfxnBsht6ZGItbztmz62CN9OKEoSmmQMElxw1tX58ZRmWiM2Q/gPU/wtqcGJh7pg5YeiMjNIrJGRNYcOnQop8EqykiRHFCE/G4vEkv0/lm74kaGc8OKE4untU0fjXR2DnucSmmzYWuAk+RVfLNm5DuUguXEhQFqfS2serks36EULSLyDqDeGLM2fXOWpmYA+zJfW8cQSsnimEznhqalKIriYUyqmHAg4P5bVefG4BnwwMQYc7sxZqkxZun48eNzHJaijAwpccPnDjTiWWtu+FI5cEn8kugphKhzQ8khGw6MY2H1m2Db+Q6lYLEsWDZtD6vqZ6vAOHiWA+8SkV3AH3DTUb6N6+pMTuNQB+zzlvcAUwG8/dX0do4COoZQSpuEAxYmzbnR88ollu7c6NLaSopSUiQSJHDHd35P3FDnxtE5mEw38Z7rve2pgYlH+qBFUUY93Wkp3lSwGeKGMRAjkMW5kSFupAsaKm4ow8iRI7AvOlZnShkAy880vMpcDj++Pt+hFCXGmFuNMXXGmBnAtcBjxpjrgceBq71mNwD3ecv3e+t4+x8zRisqK0omjiPYkkiJG71mS0mbWj6zHoeiKKOcNHEjEFTnxkBJH4BkDkze782acgbQnExfUZRSIDMtJVPcSCqnvZwbVoKYo84NJfdsWOt+nxbO12vGo7H8quMAeO5Pe/Icyajjs8CnRGQ7bk2Nn3vbfw6M9bZ/iu5i5YqipJFwBEsMdtATN7oynRvdY49YNE+3bBVFyQ9ZxI1EPD9jPt/Rm4w8InInsAIYJyJ7gC8CXwX+KCI3AW8C13jNHwQuBbYDEeADIx6wouSRpCYRCrgDjVjGbCnJ/dnEjXZNS1FGgI1PHgYmsXBZZb5DKXiWXjQWHzFWPWN4R76DKXKMMU8AT3jLO4DTsrSJ0j2eUBSlDxzjihuptJSuvguKqnNDUUqMRKK75kbIvbZwYgnyITUUpLhhjLmuj13nZ2lrgFtyG5GiFC6ptJSAl5bi9DRk9Slu2Alijt27Iai4oQwrG16MUEMjk8+cnu9QCp6yMjh17Bus2jnZzSnTWUkVRSkAEo5XUNTn9knxWM8xhTo3FKWEcZzumhtB9zok0RknH1JDMaWlKIqShaS4EfS7A43MtJS+xQ2HWELFDSX3bNgaYCEbkLkn5juUomD5ojZWxxbRte2NfIeiKIoCeM4NDD7vWqV3QdG0mhudKm4oSkmRXlDUEzecrvzMCa3ihqIUOUlxw+8HH7FeU8GmxA1/z+MCVoIudW4oOcYY2Lh/LAsrdkF5eb7DKQqWX1JNlDAv3bk136EoiqIAnnPDclITXvWquZE29oipuKEopYWXlmKJk6rL43TlJz1NxQ1FKXKS1lCfz+CTBHFnYM6NoC9Bp+Pv3RBU3FCGjTffhJZYGQunNuU7lKJh2T+4E4A9+0h7niNRFEVxyXRu9JeWEu/M0zQJA0BEporI4yKyRUQ2icjHve1jROQREdnmPdd620VEvisi20VkvYicmt9PoCgFiOfcsC2DHXAV0ESeRE4VNxSlyEnePfH5wC9x4gN0boR8MaJOILVuumL8Iz/mWc5UcWM42bwZPvGJ/M2JlWc2vOJ+7oXzS/PzD4ZJU33MDO1n1cbqfIeiKIoCdDs3UmkpmQVFE0VTcyMO/Isx5iTgDOAWEZmHO1PSSmPMbGAl3TMnXQLM9h43Az8a+ZAVpcBJEzesgDo3FEUZArHOpLgh+CRjete0/b3FjUQPcaOjLcHt/CMP8A4VN4aTX/4SvvMd2Lkz35HkhQ2rWgBYsKwqz5EUF8tnH2RV83xMa1u+Q1EURUk5N1JpKRnXLelpKZkpK4WEMWa/MeYlb7kV2AJMAS4D7vCa3QFc7i1fBvzauDwP1IjIpBEOW1EKG6+gqM/qTktxC4qOPCpuKEqRE/funvj9uGkpmc6NaHJ/TwtpyN9T3Ii2u+0aqYXOzlyGnBdExBaRl0XkgRF947Vr3efXXx/Rty0UNrzQzlTepPotc/IdSlGx/BwfBzmOHfdtyHcoiqIoJEyGcyOzoGi6uFEkNTdEZAZwCvACMNEYsx9cAQSY4DWbAuxOO2yPty3ztW4WkTUisubQoUO5DFtRCg+v5kYP50ZM01IURRkE8c6MtJTMmhspcaPn9pA/TtQEU+vRiPs6jdSOVufGx3Hv0IwcxsBLL7nLJSpurNsSZDHrYMGCfIdSVCy7dhoAq/5cn+dIFEVRPOeGpDk3Mmtu9CgoWrjOjSQiUgHcA3zCGNPSX9Ms20yvDcbcboxZaoxZOn78+OEKU1GKg1RaCurcUBRlaKRqbviTaSl2j/3d4kbP40J+Bwc7ZS3taB+94oaI1AFvB342om/8+uvQ3Ny9XGK0t8Or9WM4tep1qKnJdzhFxfwzq6iyWnn2xZGfI15RFCUTxxFsSZsKNp7h3HCKIy0FQET8uMLG74wx/+ttPphMN/Gek8ryHmBq2uF1wL6RilVRioKkuGEbrKB7waHODUVRBkV6QVGfOMQza270JW4E3O3RqLse7XBvRIxGcQP4NvAZoM8RV04spcmUlGCwJMWN9evBYHHKbK0bcazYNpw55U1W7ZsBieKweCuKMnpJGAvL6hY3YrGehoZ4kTg3RESAnwNbjDH/k7brfuAGb/kG4L607e/3Zk05A2hOpq8oiuLhiRs9am50qbihKMogSIkbAQu/Fe8tbnS5ooU/kJmW4h7XLW6MTueGiLwDqDfGrO2vXU4spWvXQiAA555bkuLGy6tdW9Cpp/uP0lLJxvLTE2xyTqLpmY35DkVRlBLHMYItDkEvm7UrnpECWzzOjeXA+4DzRGSd97gU+CpwoYhsAy701gEeBHYA24GfAh/JQ8yKUtg4jltzwybNuZGftBT1uypKkZN0Zvj8rnNjwGkpgZ7iRkfEfR5t4gbuQOZd3uAlBFSJyG+NMe/N+TuvXQsnnwxz58LTT7s1OCRb+u7o5KUnWhiLQ92yafkOpShZduVxmLstnvv9Ti5566J8h6MoSgmTMBaWQCjkrke7MsYajo3gYLBSN1UKEWPMM2SvowFwfpb2Brglp0EpSrGTNhVst3MjPyKnOjcUpchJFvXy+wWflejt3EhOBZvp3Ai6x6WcG2kFRZ3o6BE3jDG3GmPqjDEzgGuBx0ZE2AB4+WU49VQ44QS3AMXBgyPytoXCyy/DKbyMLDo536EUJae/cwI2cVY9lZ+7H4qiKEkcI1iWSTk3OuI974/GEjZhcQcUBe7cUBRluEkrKKo1NxRFGRLpBUX9VoJ4pnMjKW4Ee/7cezk3vGcHm9a20nEX5IxoFBobYfp0V9wA2L49vzGNILEYbNxdxanWK3DiifkOpyipqIBFY3bz7OvHua4fRVGUPJEwFrYYRCBodRGNZYgbxqbMEzcK2bmhKEoO8KaC9fm0oKiiKEMk6dzwBazsaSlJcSMzLSXTuRHt3tfY0vM1RgvGmCeMMe8YkTc7fNh9HjeuW9woobobmzZBV8LHKXWHen/5lAGzfFE7L8ROIbaldIQxRVEKj6RzAyBkxYhmODfijkXY7nSX1bmhKKVFelpKyB3zJWKalqIoyiDoMRWslSBu+khLyXBuJK2lyVlSotFut0Zjq5bjGTLJGVfGjYMZM8CySkrceHaV+706/Qx1AQ2F5e+oIUI5L/9+S75DURSlhEk6NwDCvi6iiQznhuMjbLkprercUJQSw3G8qWDBCgXcTQNwbnR2Dn8oKm4oSpETjx0lLaWv2VKSzo12t/PpSBc32vRO+5BpaHCfx41zZ0yZPBl2785vTCPIqkciTGYvM1bMyHcoRc05104B4MmHO/IciaIopYxDmnPDjhGN9xwnxIxN2OeKG0lHqaIoJUJazY2BOjeeeAJqarrvBQ4XKm4oSpGTFC9c54bp27mRKW4kK563u8UKo50qbgwr6eIGwPjx3dtKgGdWwXJWIW9Zmu9QippJk4UTK/bwxOYJ+Q5FUZQSJmEs7KS44YsTTWSIG46PsB0DIB5XcUNRSor0mhsB19XlxPt3brz2mpsSv3//8Iai4oaiFDnxpLgRtPFZDjGTYRVNzqaSWVA07IoZSedGD3EjEshZvCVDprgxbtzwy9MFyu7d8GZDOWdZz8HChfkOp+g5d2EDT0eWEN89zCMARVGUAeIYwfKGCSFfnA6n5zghZnyU+V1xIzZ6JlxTFGUgJJ0bNthht29IHMXB1drqPncMszFVxQ1FKXJSU8EG+potpQ9xI5WW4jo7Ojq79zdGQjmLt2RoaAARGDPGXR83rmScG6tWuc/L5xzqLu6iDJoVb6+glSpe/s3GfIeiKEqJkjB2mnMjQTRD3Igbm7DfdYKqc0NRSoz0qWAHUnPDGFq37AFU3FAUJYMes6XYDnHTR82NTHGjwnV4dCbTUrosfMTwEaOxQ8WNIdPQALW14POcNKUkbjxjKKeNRWdX5TuUUcFb3z8dgCf+2p7nSBRFKVV61Nzw9xY3YsZHwO8gOMRi+YhQUZS8kUgQw4/fb7AD7nVIoj+R87HHaP35XYCKG4qiZJC8Q+ILWF5aSoa40VdaSrV7Rz3a6vpHO7pswkSpsVtpjKq4MWQaGmDcOFauhKlT4c79K6C5GbpGv1/3sYe7OJPn8L3llHyHMio4bqqfuWVv8PiGcfkORVGUEsUxFrY3jAgH4kSdnq68GD78PvATI67ihqKUFo5DhDLKQgbLdvPXnHg/BUUPHaKVSkDFDUVRMkjV3AhYXkHRnuJGlzfNUi9xo8YVMKKtnnMjZhGWKLW+Vho7y3IcdQnQ0MC9cgUXXQR79sCvN3uFNQ8fzm9cOWb7dti8Pcg7eADOPjvf4YwaVsw7xNOti4g3NOU7FEVRSpAEVppzwyFq0pwbjuPdtQUfcXVuKEqpkUjQTjnlZQ6Wd7nRr7jR0aHihqIo2UkOInwBC7/tEM8sKJpWcDSdbnHDfYFozCZkdVLrb6OxszzHUZcADQ38svkK6urgxhvhye2TiRIc9akp993nPl824Xk48cT8BjOKWHFxiDYqeem3m/MdiqIoJUi6cyMUSBAlBAkvpz4eJ44Pv99zbsTzF6eiKHkgkXCdG2FS4ka/U8FGIrTgpi6ruKEoSg/icYPgYPn7mi0FfMQQf8/tvqoyLBKpqWA7unyEpItafzuNXSpuDJmGBta0zOHss+Gqq9zzu4rlJSBuGBbZG5lx0YluQVVlWFjxgZkAPHF/c54jURSlFOnh3Ag4dBDuvrsSj7vOjYA6NxSlJElzboiA4OAk+hc31LnhISK7RGSDiKwTkTXetjEi8oiIbPOea/Mdp6KMFPGYe6cEnw+fbYiTWXPD22/33C6VFYSIpmZLicZtwlYntcEIjbGKEYt/VGIM+w/52BepZckSWLEC/D6Hh7loVE8He+iQO1PKZYl74Pzz8x3OqGLi8eWcFNrJE+tq8h1KQSMiU0XkcRHZIiKbROTj3vas4wRx+a6IbBeR9SJyan4/gaIUJg7pzg3jOjcyxA2f33KdG/1MkqAoyigkzbkBYJPov6Coihu9ONcYs9gY4yWx8zlgpTFmNrDSW1eUkiAeN/iIg217aSm9C4omxY8elJe74kbEmwo25idkd7niRlzFjSHR3s7argUALF0KFRWw/LQYf+dto9q58Yc/gOMIV/BnFTdywIoT9/N04wLibdF8h1LIxIF/McacBJwB3CIi8+h7nHAJMNt73Az8aORDVpQCx3FIYHc7N4KeuJEskJ1ybojr3Iira09RSgmTcAuKlpe7fYQlBkfFjSFxGXCHt3wHcHkeY1GUESUeIyVu+Gy3qFc6sZj0KW4E6STakXRu+AhbXdSGOmhKVGJ0mvrB09DAWpYgYli82N10/tt8vMJiWva05De2HJFIwLe/DcuqN7F4TgfU1eU7pFHHiosCtFHJml9r3Y2+MMbsN8a85C23AluAKfQ9TrgM+LVxeR6oEZFJIxy2ohQ2joODhWW5okU4lN254Q+IV3NDxQ1FKSWiUTBYlHnzEdgkUiV5sqLiRg8M8HcRWSsiN3vbJhpj9oM7sAEmZDtQRG4WkTUisubQKLaGK6VFPJ4ubpisNTeypaWknBsdrooRTfgI2TEmlLeTwEd9/Uh9glFIQwNrWMpJdW1UeCaYk09xz/+Wbb5+Dixe7rsPduyATzX/O1x/fb7DGZWc949zEBwevktnTBkIIjIDOAV4gb7HCVOA3WmH7fG2ZXs9HUMopUkiQQIbO825EcdPvKNb3EgWFPUR17QURSkx2iOuoFnulexznRtac2OgLDfGnIprJb1FRM4Z6IHGmNuNMUuNMUvHjx+fuwgVZQSJJZ0bPh9l/jhRwjhp/UksTnbnRlmZK254DveOeICQHeOkMQcB2PL9lfA//zNCn2KU4Tk3li7o7rHnzXOfN71ZmaegcofjwFe/CjNrjnC53A8f/GC+QxqVjDu+itMrNvHgSxPzHUrBIyIVwD3AJ4wx/dmlst1izupb0zGEUrKknBvuasidbI3Otgznhh/8omkpilJqdIsb7rMtTr/OjXh7Jx24No+SFzeMMfu853rgz8BpwMGkjdR71nvOSskQj3k1NwIBqkKdALS1de/vMy3FtglJF9Go2xFFHT9hX4z5E9w7kpv+tBl+/OMR+Qyjjf3b29nPZJYs6d42cyaEpJPNB8bkL7Ac8d3vwurV8B/OF7HfeammpOSQS085wOq2k6jf0Xb0xiWKiPhxhY3fGWP+19vc1zhhDzA17fA6YN9IxaooRUHSueEZQJPiRkebe/WS6IxjsPD5wScJ4onCFTdE5BciUi8iG9O2acFhRRkCkagrKSTTUixMv7OltLV230MoaXFDRMpFpDK5DLwN2AjcD9zgNbsBuC8/ESrKyBPviLniRk0NVSG3uFdL2n3KPtNSgJDdRWeXJ24k/IR8cSbXRKiSFjYdGg/NOu3kYNj6qttpz19altpm2zC3cg+bG0dXOv+mTXDrrfCOk17nfS3fhw9/ON8hjWouva4ag8XDP9ye71AKEhER4OfAFmNMuvWsr3HC/cD7vYuYM4DmZPqKoigemc6NsDduaHOnko9FXZHD7xd8kiAWL+jLi18BF2ds04LDijIE2jvc33x5hefcsPp3brS2di93RIa3yF9B9z5ZmAg8IyKvAC8CfzXGPAR8FbhQRLYBF3rrilISRCMOIaKuuBHMIm7E+3BuACE7TrTL7QY6nCBhfwwJBpgvW9jcMrXnCxUpfU0NmUte2+Ge6zmLy3psnzeuns2R6bl++xHjlVfg3HOhujzG7dvPQy6/HC7OHDMqw8kp71/IBA7y4ANa8bcPlgPvA87zpoxfJyKX0vc44UFgB7Ad+CnwkTzErCiFTcq54dXcSIob7e7VS7e44aalFLJzwxjzFHAkY7MWHFaUIZBybnhpKZYYnETf45TWtu4+oqM1PqyxFFVlO2PMDmBRlu2HAZ13UClJmtpsaqwWNy0l7Oa/ZhU3sjk3/HEaYm6HFHUChHwJCASYbzZwb/wyiEfdqd4CgRH5LDkiOTXkS57za62IPGKMydmUE1vfDBOWDqZMDffYPm9yM7/fcSZtrYaKysId/B2Nxkb44Q/hv/8baitjrAxezCQ7CrffDlK8n6sYsMrDvH3yk9yzbTmdnRAM5juiwsIY8wzZ62hAlnGCMcYAt+Q0KEUpdjJmS8l0bsQ7PXEjIPjEIZYotnunPQsOi8jRCg6ru0tR0sh0bhxN3Ghp774mccUNf59tj5Wi630URelJU7ufGn87ANVht+bGgJ0b/gTRmA9jXOdGyJ+AYJB5ZhMNjOcQ44o+NaWfqSFzxmuHapkd3J2y8CaZf3wEgC0vDXOCYa7YuRM++lFazryIB47/Z/5lwUOcOu0QY8ca/u3f4KJpm3m2fTFzujbC/feDFlkcEa5+ZxctTiWP3KGlIRRFGQESCRyslHMjXOaJGxE3p76Hc8OKE3dGjcg94ILDOpuSUspEOl2xoqzCHfjaYvpPS4m47cNEUrV7hgsVNxSlyGmOBqkOulOeVIXduygtzd3/e2OJ/sQNh2jcRzwODjZhv1uYdD6bANjE/FGRmpIkY2rIzH3DNjB5rXkic2oO9to+b677d9n8YhEUg/zOd9g7ewUf+dFCxr/wF96587t8f9O5VO3exBfNf/Aib+GeVxcw9azpbjXR00/Pd8QlwwWfOpkaGvnTTxvzHYqiKKWA45DA7nZulHmOz0jPtBRf8To3hlxwWGdTUkqZ9miGc8MyOP2JGx3uNckE6umI9DNl7CAoqrQURVF609QZpqbWrbWRSktpNiRvOPSblhJ0iCZ8qelgQwGnl7ixosidG0mONjWkMeZ24HaApUuXDrqgQSwGOzonc/WE9b32Hb+okgCdbF7XNdiXHxm+8Q2e+sxfuCqwnmZTxY03CtdeC2eeESBcPwM2nw6JpbBggTsNjDKiBObM4LKa+7j35fM1NUVRlNyTcm64q0lxo6Pd/VcZ63QvTvwBcZ0bxSduJAsOf5XeBYc/KiJ/AE5HCw4rSlYina6k0MO50Y9m0drpprtPoJ7mjuGdRVDFDUUpcppi5dSUu6JGT3HDJRa3CPfl3AgYok4gNQ1TOODW3JjMPqpoZgsnFX1aCvQ5NWRO2LXTEMfPnKnRXvt806dwIlvZtGVsLkMYGo88wkOfWck75TFOmGnz9L3C3LnJnQIzZrgPJa+8+6Jm7rirgr/f3cI7r6/KdziKooxmUs4NdzVU7qoc0Y6kuJGsuWHhsxxiTuGKGyJyJ7ACGCcie4Av4ooafxSRm4A3gWu85g8Cl+IWHI4AHxjxgBWlCGiPun1CeaX727csgxPru31S3JjIQQ5ETxrWWFTcUJQiJhqFqAlRU+UOLCrL3OeWZgdwO5p+01JCbiHRlHMj6Do3BDiB19nB8UWfltLP1JA54bWX2oBK5szKIlnX1TGfB3nhjUtyHcbgaGpiw3u/xrute1mwwOLxJ4WamnwHpWTjgk+dzPi76vnlN9pU3FAUJbdkOjdS4ob7fy7ew7mRIF7A4oYx5ro+TGw1JAAAIABJREFUdmnBYUUZJO1J50al2zfYVj/ODWNojbmW0wnU09E5vP1F4fY+iqIclaSpoqbafbb9FuW00ZJmtoglrD7TUoIhIWqCaWkpJjUzygx2sYsZo8G50dfUkDnhtXVu0dA587Jox1VVzPNvZ9eRKtrbcxXB4Gn/1//givofUzk2wF/+aqmwUcAE3rKIG8Y9yF/WT+Ng7/IuiqIow0fSuWF7NTdS4oa7O5WWEhR8llPQ4oaiKMNPpNMmQCe+oNs3WBY4fYkb0SitVOKzEtQEInTEhtdrob2PohQxTUfcnqO61vspWxZVtNDS0jMtxU+MXlN34E7nFiOQutAOe84NgJll9exiBqa5uJ0bxphnjDFijDnZGLPYezyYq/d7bXOcMRxm7OwsOYQizBt/CIPF1q25imCQbN3KZ38+m9eZxZ13B6iry3dASr+IcNMHDXHj49ffqj96e0VRlEFi4gkMVmoYEa50L0a601LcsYjPb+G3HGJO75spiqKMXto7fZTTnrrWsC1DItHHrEmRCK1UUhmKEQ4LHXE/ZtCV7nqj4oaiFDHN+12XQM1YbyBh21TR0sNsEXMs/BIH6d3JJIuCNR1yE+NCwTTnxqROOiijfl88h59g9LFlu58T2QoTJ2bdP2+aO1PKpk0jGdXReerm3/IDcwsf/z8Rzjkn39EoA2HuJy9hOau4/WdW33dIFEVRhogTdzsY27vBGqrwxA3P9Rnrcq9M/EELn63ODUUpNSIxH2VEUuKGZYFjIKtqkS5ulAmOsYj1U5/jWNHeR1GKmKa9ruWiZrzf3ZBybnS36YzbBCS7QJG0ljYdcEco4VC3uDFzqnvMrt16B2agGAMb36x0Z5vpQ9yYNVvw08XmzSMcXD/En36OW556N9Nrmvivb5flOxxloBx3HB9b8izbG8dx/5868x2NoiijFCfm1vNKTQXriRsdSXEjlZZi4bMMMUdL+ilKKdHe6XedGx62BQ4WxLNcf0QitFBFZVki5QJLTmwwHKi4oShFTNN+tzeoOS7kbvCcGy0t7gDEGGjoKGe8fTjr8Slx46B7YRQKkZpXMjkhxs4D4dwEPwqpr4fD7WEWWFugtjZrG//U45jDa2zeNIwevKFgDD+88UU2spDbfhCkTLWNouKqr5/OTHbwtVubhtXWqSiKkiQRdzuXpHMjWOHeUIlG3bFGvKtb3PDbDnGjlxeKUkpEYj7KpFuhsGxIYJPVktHR4To3yhzC1YHkpmFDex9FKWKSokTNJE+ASIobre5qSwtE434m2g1Zj0/efWmq73LXQ3Snpcxxn3cdKs9R9KOPZKrJ/Np9WWucAFBXxzw2s3ljYuQC64eDv/k7/77jBi48aTeXX6dCVrHhO/ds/mXa3Ty/cyLPPKW5KYqiDD+Zzg1fWQAfMaJd7npmWoo6NxSltGjv8lMukdS6ZUGEMs65IMBzz2U0TqalVDiEa9wbqipuKIoCQFODq4jW1FW4G5JpKa3ugCM5i8LEvpwbnrjRWO++TjhMStyomD6Wcb5GdjZW5yj60UdK3Jjc2HcjT9x4fZc9rJ35oIjH+dw/R4hQxvf+eFy2sixKoSPCB758AhM5wOc/fFjdG4qiDDuZzg38fkJEiXb2FDd8QRu/bdS5oSilwsGDcMUVRDqEMroHtbYN25nF08/5ePzxjGNS4gaEx7p24Y7G6LCFpL2PohQxzYcT2MQpm5ScC9ammmZa2tyfdlLcOM7fh3OjyhUymg67d2VCYYETToBp02DpUmaED7KrdWxuP8QoYuNGGGM3cVxdP3etpkxhEa9gjLBu3cjFlo3n/v1v/Kr5Cj51+U5OXODPbzDKoCl7z+V8cdJPefrV8Tz4l8JwBCmKMnpIFhRNOjfw+wnTQbTTHWv0dG4YYkadG4pSEjz5JNx7L+3NccqtdOeGsI/JAOzfn3FMUtyoShM39vdzU/AYUXFDUYqYpiZDDU1IbY27IZWWIhiT5twINGU9PiVuNLoDk3AYmDIF3ngD5sxhZtVhdkUm5PpjjBo2bYL59qvIcdmLiQJQV8cyngVg1aoRCiwLidYIt3xzBnWBg/zbr+fkLxBl6Ng2H/rOQmbzGp/9SEvW+l2KoiiDpXu2FE/cEHGdG12euBHzxI2Q7dXc0ELkilIS7N0LQHtXgDKr231h2xDDvcbIFDectggHmcjEiRCeUAlAx4FmhgsVNxSliGlqtqihCaqq3A1eWorjCJEIHDjgbp5Yl/2ufFLcaGz2KqCX9cxLmFHTzK6uyTrN5AAwBjZuNMyPvdLnTCkAjBvHxEATJ9Q08OyzIxdfJj+58mFeji3kW59vpKJS81GKHf/Vl/H1WT9l095avvMNnTlFUZRhYvduEkfcCw/L7v5fEZJOOrpcESOeJm74fIY4fk2RU5RSwBM3IoQp71FQtLuvyBQ3GuodughSN9VKEzdaGC5U3FCUIqap1abGbu0uXuk5N8AtJnrwIFgkGHd8VdbjQzWh1OsAhMI9u4SZ49voItjbUqb0Yt8+aG4WFpj1MH163w0tC6ZMYVnNZlatyj4FeK5588GN/N9Hz+W8yVu45gtzRz4AZfgR4bJfXs47uZ9//6Lwxhv5DkhRlFHBihU4X/pPwL0bmyQkXURjybQUd5s/ZOP3MlISmiGnKKOfffsAaKeccjut5kZaZlrmNcTufW5HMnWGTdhLq+841DZsIam4oShFTFPET42/e17ppHMDPHHjgGE8h7BnTM16fFLcaGwPIDgEwj2tpHPq3Py5VzerdeNovPii+7yIV2DRov4bL17MsrZHqK+HHTtyH1s6idYI772mk4T4uP0vk7WI6ChCzlrO9698HIl1cdN17eq4UhRlaLS3w44dJF7dBoDlS3NuWJ1EY+6YITnboz9k47NdxV7T4xSlBNi7F4M7M0qZdKelZDo30m/k7Tnousnrjg8QnlQLQEdD2rXMEFFxQ1GKmOZokOpgmgU907mxu5OJHOzTSRCscaf+PNhWTogoEuiZvjJvpqvCbnq5KwfRjy5WroQyfxensRoWLuy/8VlnsbzhXoART03597Me4+nIEn74Lzs44VSdCWe0Me37n+HbZZ9n5XPlfOvreutUUZRjxHFg6VK44w7YsYMXeQvPOacBYKenpVhdvcQNX8iH32d6bFMUZRSzdy9dBEjgo9xOr7nR3VdEo9CcVlJj9yH3xurUWUHC48oB6DgyfNMHqrihKEVMU2eYmrK+xY0De+Icx4E+xY3qiW4H09BZxUx2gr+nuHFcnY9ajrB5g96CORorV8I5YzYSmDUNKir6b3zWWcxjM1XhGE89NTLxAfzi2r/zX+vfwYcWr+a9Xz955N5YGTkmTeKmX57FldzD5z9vRvT7pSjKKODNN3lo7TjW/X4zvP46/8SP+D/8FMiouWF1EY273vMOb0rYQNjG59nR1bmhKKMcY2DvXiK4M56U2d3XI8m+wrZcsTM9NWX34TL8dDF+okXYq/WnU8EqigJAU7yCmvK0EURmWkq9uM6NadOyHj9psrAy9HaeP+WfWMNSCAR67JfqKuaziU2bNXehP/buhVdfhfPjDx89JQXglFOwy0JcOnU999zjqtq55q6PPMnNd53HhRNe4YfPnarpKKMYefc1/Oz6Jzje2c6Vb4/y+uv5jkhRlGLBbNjI9fyOz656F7GtO9jIAo7gTgmfLm6E7S6iHUBdHatfspnEPsZM8OH3e86NP9wDr72Wj4+gKEou6eqCZ56BxkaIRmmvqQPo6dzwUtjmTXdraezf784o2NYGe5rKqbP2YVneLI1AR/PwOcRV3FCUIiXWZWh3yqipTkusT3NuND/1CgcbA/2mpQCcV7WG053nCBPt5dygutoVN7YFtfJ5Pzz2mPt8/uE/Dkzc8PvhjDO4yfkZjY1w7725je+n73mc9/zoLJbXbOKeTXPxh3SavtFO7S++xV+Wfw3T1s4FZ7Syc2e+I1IUpRjY8dQejjCWNe1zefWFZroIpvbZ6TU37BgdbQnM3r08cWAub+VJxLa6nRsf+Rh85SsjHb6iKLnmZz+Ds8+GRx4BIHLyGUB258apM5sAV+dcsgS+/GXY3VzNVJ87nWNK3GgZvjw2FTcUpUhpXuMW+KqZPaF7Y5pzY89P/ko07ue4YCNU91NbYdw42LXLXc4ibsxjM42tPg4eHMbgRxmPPgpjq2MDKyaa5KyzOO/1nzJ9msMvfpGbuKKtMW5Z9Aw333kub5uwjr9tn0PluODRD1SKn0CA2Q99j7+f9gWaG2Kcc3Ija58ZvpxWRVFGJ6ufc92gRxjLvY/2TLHskZZix4kSZDuz2M9kVlhPgwg+TwD5M1fw94f1roiijDqSd/R++1sA2ucuAXo6N5LFh0+dcQSAe+6Bzk43hXtPWzV1wXrAveywxKGjbfhqhKm4oShFSvPKNQBUn3pC90bbppJWAF7rcG1iE8cb+s1BuOKK7ko/meJGlZuWAq6dTOnN/v3wpz/Bu+Ztx8IMXNx429uwTIIPzF7Fo4/Cxo3DG9fTv9/NKRP38sP1Z/HpU1fywJuLKBsbHt43UQqbigqWrPouj7/3F9DWxrKzLb55wwa6OvWCQ1GU7Kx+rXvq+J+3XkPAihHEvWjp4dzwxYgS4kneCsBbfasAUmkpH+N73HTwKzi797rVRXX6JkUpXvbuhT/8wa2z8dRTbOYkbvzrNXQQIjLLreFW5utOLbF9rsQwd2IjoVC3HvLSS4bdkbFMDTUA7uVJ2Bejo92ha5gyU1TcUJRiwhi47TZ45RVW/+0QAHWnpjk3bJsAMWaxjf/lSgAmTjrKz/zGG7uXM8WNE05g/hz3Ls6mL9091OhHJV/5CsRihs+br8CECX3WN+nF8uVw5ZV8eNX7GDcmwXXXQSQytFgSCXjo7jYuOeE1zrl+Ku1RHw9/9jG+sfZ87KDv6C+gjD58Phb95tO8fN9u3lbxLP/664XMr97ND25cTdPBzqMfryjK6McY2u97FHP4CKsbZrJkwpuE6OANZjB//CHml7l5bT2cG74EEcp4vOKdTOQAJwbcNknnhsFiD1N57qcbYfFi+NCHRv5zKYoyeBIJaHLTSvjkJ9ly3Zf41vVrMYcO8Z++L3GHeT93ch1HjjsJgHJfWlqK1w9MKm9h0iRX2yzzd+E4Qtz4qCtvTLUNBxKsjZ/MtKnDUwR91IgbInKxiGwVke0i8rlhedFdu+CnPx2Wl1KUQROLwfe+Bzt2wN13w6c+hbnm3Xxz9QrmVO5jxblprgzL/Ul/r/aLtOPaSSfOLOv/9WfNcnPnoLe4EQox8eWHGBNsY93TLfDgg8P1qUaUnPQPwNq1cPvt8KELdnHC87+DL3yhf5dMJt/+NhPtBn4z9pNs3AjXXQcNDQM/PJGArVvhZz+Kce25B5lU0cIl11SwZkctX1v0e17dZvO2r5537B9MGXWMe9cy7j9yNg9+8hGqaeGjd7yFccfZnH3ca/znP2zk6T/up7mxdO+sDncfYeIJWj/wz/CTn3gbDFq4SCko4nFe+dajdNS3susHf2X65Yt53+znecmcwrJFERaHtwKwaFY7iya6+fG2v/uyYWywjVaq+H3bZW69Db8roPvPcC3qN77fIUQHd922ly9tvpr3/fJcYuu3jPCHHB5yNYZQlIIgHseJO+6/qBdeYMcdT7PuJQcuu4zvTvkaV721gda7H+Za/sCn71zKN/k09zhXAPAD++N8544axtLAyeXd1cuTzo1J7GdStXvn7ib7Dny4tTWmVjal2oaDDk+ygo4Ow7x5Q/84o+JWnojYwA+AC4E9wGoRud8Ys3mwr3nkCLz2uXuZedd/w7r97G8KM2HT41T984202dWEXn6O4KXn0xGswWzYiMyfhzW2Fmv7a1jT6rDGj8Xa/QbWuDEwZgwcOABlZZiaWvfF/X6oqXGntACoqoKODlfaqqhwE5MSCSgvB9t2r5eSgyPH6X4YL+XAsrofIv1eYA10fDUs7TJ2Gsf0uS99vUe7ftoa0//r9GiXJP1c2bZ7HhMJd96yRAKMwQoHEUuwohGscBBHbJzmVhK+II4vgNPUghX0Y1eEsZqOYPst4mVVdOw5TMDnYE8cR9vOQwRNFP/0yTS9egB/RwvlC2ZyaP1+rPoD1JwxlwPrDuC8+hrHve1k3lxTT9tTaznh8pPZtraF/b95hCVXzWTdliDrnu/goi99ksdjZ/En30aWbXuKtSziJ5c9jWVN7v5stlso8uJ3+nnPhs3c+fJcppx4lGlJAW6+GZ5+2v2uZiBlYS65MsGv7ryRC276V97zxgW9ZlUpZHLRPxjj1lP62MdgwgTDF7ZcD/PmwYc/fGwvNHUq/OY3XHTDDdwWKudfH/gyc4+Pc9UFLZx+ShcTxhuwLDojcaKtcerrDXvfdNizV9i6t5wt9WPpdAKAn8nEudj+C+9a0cS7vn4Wgbe8Z7AfTxmliN/HJf9zIZd802H195/j3tvr+fuWqXzxj4sxf3QHItP9e5lXu5/pY9qYdlwXU+qE2slhqusqqZkUpma8n3BNEKsshBUKYOH2n23NCTrb4xw/LQ51dXn+pMdGLvqIe/9suPn3/8l//+rTzHqxjOceaODsinVM+eKH2Pr3N5ge2M+EGy5h37p6qqMHqb34dJp2txJsOkjl6fOINMewGuopm+s6wZz6BsyEiTiWz61QX12NhIJISzNSFkbCIaSt1d0WDkF7O/h8EAq50zHF493jguT/PtvuXs4cX3SfnOyPbAyHeDPYaZz6GwccbX0ox2Zbh6OPMxzH/duIuH+rUIhozObwm+2UVfsJVfqpf72VUKWfqgkh9m9pwg7YjJ9Rzp71R0gkoG5hLW+uqae9JcHsZePZ9fwBDrzRyaJLJrNz1T5eXdfB8qsns2vVHp5YmeDCa2rZu3ofv3hoEm8/p40jWw/xrR1XcMoXXsUXm8IRxvC7xksBeMs5Ydi5k+e3L+bkxRZWuB129nRufOb4u5m16xEeuvrn3Pz4nWC5lxSnvWcW/7AOvvEti5aH1vCT+utTRUnLr3yUH207qahm68pF/wAQjTj85sftPP2kw7XXwlnLHNr3NOKrrSRU6cdubOBgRxWddhmT7HoOtYVpNlXMqGigqd3P4UQNM6obiUbhQGct02pacLri7O8cw6TqCP5ElL0dYxhXEaWMCPsiNVSXx6my2tjfUUNZ2FBjt3KwvQJ/yGasv4WGSBn4/YwLtNAUDRG3g4wLttLa4aPTCjM21E5HVIhIOWPDEbqiDm1UUBvuwOmM05yooKasC4nHaI6XUxGM4Xc6aewqpyzkEKaDxs4yQiEImwjNXWF8AYsKaac5GkQCfqrsdlqjfhxfgGp/hPaoTdwOUuXvINopdEqIqkCUWJchYsJUhbpIdMZpN2VUhmLQ1UWrU055IIYd76Q5VkZZ2BBIdNDcFSYYEsJOO03REP6wjwraaI4GsYJ+KqWN5ogfEwxRbbXS2m4RD5ZT62+jrQ2ivgrGhCJE2+K0SSVjyqJ0tXbSQhVjKrpw2iIcSVRTWxnHamvhUKyG6ipDKNrE/vYqKmtsKqIN7GutpGxMiNpYPXsay/HXVjDRHGBPQwhTXcMU2cfegz46q8czM7ifvbsdmssnM6dyPwd2dnAwPJ15Yw5y+PUmdlizWDj5MG2v7mGDM5+TZ7TgbH6VVe2LWTg7SsWmF3jwyOnMmyfUbXyI3+9fwYy5YRZv+A3f23sl40+o5JJ1X+UzTbfir63klobf8rnEl4mIw03mHfyED8NTsI6X2MEJTOQAn+Eb4MAnuI1vJz4JK+Hb435MZSDduWHhp4sxX/wYkxJjgau5NHoPq6uX83zzPKbWtKbaJouKfm7504y7/3X44AeH8tMCY0zRP4AzgYfT1m8Fbu3vmCVLlpj++N//Tf6n14c+Cusxd1q7AWMmcMB0rH+t5xf3mWfcRr/7nWlfv9085TvXmAcf7Pe7bowxxnGM2bq1z93t7casWNhgLOLm7zf/6eivZ4wB1hgzOvuHzk5jFi0y5sILjal/cacx8+cb8/DDAzovWdm+3ZirrzYbgkvMldxtqmjq8+8fImJOYJu5mAfNp6t+bH558v+YTR/8pnHuvc+YtrbBx6CUJrGYOfS31eaBj/7N/PdZD5jrJj9uFoe2mLFy+Jj7pmnsMqa2dkBvWyj9g8lRH/HKK8actSye9/8X+tAHGCMkUsvTfbtTy9fMWpv6f/P7L79uVlS8aMCYLeui5jdX/dmAMY/98ZB58ta/GTDmgZ/s6f6SX3aZMTNmuMtXXWVMXV2v38Fd777bgDFvf8sB86+nP2nAmJ98ce9R+wfj/hALoo/IRf8QiRgz2XfAgDFltOX9+6GP0nkEiKaWx9CQWp9uv2mm4PYNCyt3mgv4uwFjls06YL7M/zVgzAVnRcxq6zQjJMyFFzqmZcIJptLXbmbONCZ67sXGLFuW+o7fcF2nmc5OY8aNMx8//n5jEzMtZ19qPvcx9/tef8XNqbZL5raZKew27YSNmTLFvSYZQv8g3g+1qBGRq4GLjTEf8tbfB5xujPloRrubgZu91ROBrcfwNuOAYzCM541iiLMYYoTiiLOQY5xujBmf7yBGqH84GoX8d8pEY80dxRRvrmMtiP4BRqSPKJS/u8ZRWDGAxpFJehwF0UfkuH8olPOejsY0MDSmgZGrmPrsH0ZFWgqQzeDWS7UxxtwO3D6oNxBZY4xZOphjR5JiiLMYYoTiiLMYYiwAct4/HDWAIvo7aay5o5jiLaZYh4Gc9hGFci41jsKKQeMo3DgyyFn/UIifV2MaGBrTwMhHTKOloOgeYGraeh2wL0+xKIpSWGj/oChKf2gfoShKX2j/oChFxGgRN1YDs0VkpogEgGuB+/Mck6IohYH2D4qi9If2EYqi9IX2D4pSRIyKtBRjTFxEPgo8DNjAL4wxm4b5bXJiV88BxRBnMcQIxRFnMcSYV0aofzgaxfR30lhzRzHFW0yxDokR6CMK5VxqHN0UQgygcWRSKHGkyHH/UHCfF41poGhMA2PEYxoVBUUVRVEURVEURVEURSldRktaiqIoiqIoiqIoiqIoJYqKG4qiKIqiKIqiKIqiFDUqbgAicrGIbBWR7SLyuSz7gyJyl7f/BRGZkbbvVm/7VhG5qNBiFJELRWStiGzwns/LVYxDiTNt/zQRaRORTxdijCJysog8JyKbvHMaKrQ4RcQvInd48W0RkVtzFaNSHP3HUGMVkRki0iEi67zHjwsg1nNE5CURiYvI1Rn7bhCRbd7jhgKPNZF2XnNepG4AsX5KRDaLyHoRWSki09P2jeh5LQYK5fc/hN/2WBF53Pu/+/08xTCs45QhxHFa2m/xFRG5Ih9xpO0flvFQIfT7Q/ydjNi4a6jkoj842mvmKqb+fpci8oT3msnvxoQRiKfP76OILPHi3C4i3xWRbNP35iKm69PiWScijogsHuo5GmBMxzwGGoHzlDUmEVmc9hteLyL/kLbvVyKyM+08LT6WmLJijCnpB25xoNeB44EA8AowL6PNR4Afe8vXAnd5y/O89kFgpvc6doHFeAow2VteAOwtxHOZtv8e4E/ApwstRtwCvOuBRd762Fz8vYchzvcAf/CWy4BdwIxc/d1L+VEM/ccwxToD2Fhg53UGcDLwa+DqtO1jgB3ec623XFuIsXr72grsvJ4LlHnL/5T2HRjR81oMj0L5/Q8xjnLgLODDwPfzdC6GbZwyxDjKAJ+3PAmoT66PZBxp+4c8Hhri+ZjBMPT7Q4xhxMZdef6cWfuDgbxmDmPq83cJPAEsLZTvI/AicCYgwN+AS0Yipow2C4EdQz1HxxDTDI5xDDQC56mvmOYAs73lycB+oMZb/xUZ46KhPtS5AacB240xO4wxXcAfgMsy2lwG3OEt3w2c76ldl+FeRHYaY3YC273XK5gYjTEvG2OS83FvAkIiEsxBjEOKE0BELsf9EeZyJouhxPg2YL0x5hUAY8xhY0yiAOM0QLmI+IAw0AW05CjOUqcY+o/hiHWkOWqsxphdxpj1gJNx7EXAI8aYI8aYRuAR4OICjXWkGUisjxtjIt7q80CdtzzS57UYKJTf/1DGCO3GmGeA6CDfezhiGM5xylDiiBhj4t72EO7/0sFSKOOhQuj3i2XcNVRy0R8M5DVzElOOrh+G/fsoIpOAKmPMc8a9Wv41cHkeYroOuPMY3ndIMR3rGGgkzlNfMRljXjPGbPOW9+EKx+OP4b2PCRU3YAqwO219j7ctaxvvH18zrno8kGPzHWM6VwEvG2M6cxDjkOIUkXLgs8CXchTbkGPEVR6NiDzs2a4+U6Bx3g204yqjbwLfNMYcyWGspUwx9B+94ujn/frrR2aKyMsi8qSInJ3DOAcaay6OHQxDfb+QiKwRkee9C5pccqyx3oR7d2cwx5YChfL7H64xwlAolHHKkOIQkdNFZBOwAfhwmtgxYnEM83ioEPr9Yhl3DZVc9AdD7Sdy+bv8pZdG8IVjEMNy8X2c4r1Of6+Zy5iS/AO9xY3BnKOBxnSsx47EeToqInIarvPj9bTNX/HSVW4bjhvwvqG+wCgg25ctU63vq81Ajh0OhhKju1NkPvA1XBU8Vwwlzi8Btxlj2nJ8o3goMfpw7btvASLAShFZa4xZObwh9hvDQNqcBiRwrV+1wNMi8qgxZsfwhqhQHP3H0eIYSJv9wDRjzGERWQLcKyLzjTG5cgQN5dwU4nntj2nGmH0icjzwmIhsMMa8ftSjBseAYxWR9wJLgbce67ElRKH8/oc8RhgGCmWcMqQ4jDEvAPNF5CTgDhH5mzFmMK6WQhkPFUK/XyzjrqGSi/4g203oY/nd5up3eb0xZq+IVOKmT70P1wmQy3iyfh8H+Jq5isndKXI6EDHGbEzbP9hzNNCYjvXYkThP/b+A6x75DXCDMSbp7rgVOIAreNyOK+z+v2N53UzUueEqT1PT1uuAfX218az+1cCRAR6b7xjh8/S8AAAgAElEQVQRkTrgz8D7czhoHmqcpwNfF5FdwCeA/ysiHy2wGPcATxpjGjzb9oPAqTmIcahxvgd4yBgTM8bUA6twL1KU4acY+o9ecfTzfllj9ayyhwGMMWtxFfc5eY41F8cOhiG9X9L264mPT+DmOeeKAcUqIhcAnwfelXanbqTPazFQKL//IY0RholCGacMy7kwxmzBdUAuyEMcwzkeKoR+v1jGXUMlF/3BUPuJnPwujTF7vedW4PcMPKUuF9/HPXSnT/b1mjmJKW3/tWS4NoZwjgYa07EeOxLnqU9EpAr4K/Bvxpjnk9uNMfuNSyfwS4YjPdsMYwGPYnzgqsI7cAv4JAukzM9ocws9C8n80VueT88CQDvITUHRocRY47W/qpDPZUab/yB3BUWHci5rgZfwio4BjwJvL8A4P4vbQQhusbjNwP9v787j66zrvP+/vmnSpG3SJWlS2gbaAgWUrZQCRRRZhAKjUgURuGFQcVDvGwbGFVxG3EbUGUXnN+oNgjAqmyADtwKyyCprgSItWxewTVuS0D1pk7bJ9/fHdVJKSUuSc51zcpLX8/E4j+tc17mu63yunORq8u53OSDXn/9gfBTD/SOlWmu7aiMZTGoZUF3IWrfZ9xrePpjWq5mf1zGZ5/211jFAeeb5WGABvRgsLkffAweR/NI4dbvtef26FsOjv/z8Z1PHNq9/guwGFO0Xv6dkWccU3hxQdBLJL+5jC/WZZLZfSnYDihb8vp9lDXn7vavA33vd3g96cs4c1tTtz2XmnGMzz8tIukJ/tpDfj8BTwEzeHCjzpHx8jTLrJSR/+O+exteopzVts+819PB3oFx/nXZS01DgPuCibvYdn1kG4HLgsqx/FrM9wUB4ACcBr5D8Qve1zLZvk/yvFSQDS/2eZICfJ7f7Bv5a5riX6cWos/mqEfg6yf8+zN3mUdff6tzuHJeSo3Ajhc/7LJKBleYBP+yP35dAZWb7fJJg40u5rHOwP4rh/pHC99Qpme+n50h+0fxQP6j1EJJfKFqBlcD8bY79VOYaFgKf7K+1Au8h6dv/XGZ5bj+o9V6gkTf/vbi9UF/XYnj0l5//LOt4jeR/IVsy36d9CtiyuL+k+ntKFnWcTXKfm0tyn5tdqM9km3NcSpa/D2Xx9Ujtvp/l92fefu/K9pHldXZ7P+junHn6/Lv9uST5T7OnSWaxmQ/8lF4Es7n4fiRpnTwvc87/Dwh5/NyOAh7f7nxZfY16WFOvfwfKw9dpR7/rnAVs3u57aVrmtb+Q/P4zD/gtUJntz2HInFiSJEmSJKkoOeaGJEmSJEkqaoYbkiRJkiSpqBluSJIkSZKkoma4IUmSJEmSiprhhiRJkiRJKmqGGyoKIYS7QghrQgh/3G77lBDCEyGEBSGEG0MIQwtVo6TC2Mn94fwQwsIQQgwhjC1UfZIKZyf3h9+FEF4OIcwLIVwdQigrVI2SCmcn94irQgjPhRD+FkK4OYRQWaga1XOGGyoWPyKZg357PwB+EmOcCqwGzs1rVZL6gx3dH/4KfAD4e37LkdSP7Oj+8DtgH2B/YBjw6XwWJanf2NE94l9ijAfGGA8AlgDn57cs9YXhhgomhPCDEML/3mb90hDCF7rbN8Z4H7B+u+MDcAxwc2bTtcDsHJUrKY+yvT9ktj8bY3wtd1VKKoSU7g93xAzgSaA+ZwVLyquU7hHrMscGkgA05qhcpchwQ4V0A/DxbdZPA37fi+NrgDUxxi2Z9QZgYkq1SSqsbO8Pkgau1O4Pme4oZwN3pVCXpP4hlXtECOHXwOskrbz+M53SlEuGGyqYGOOzQF0IYUII4UBgdYxxSS9OEbo7bTrVSSqkFO4PkgaolO8PPwceijE+nF6FkgoprXtEjPGTwATgRd4alqifKi10ARr0bgZOBXYhSVl74w1gdAihNNN6ox5YnnJ9kgonm/uDpIEt6/tDCOGbQC3wmRTrktQ/pPI7RIyxI4RwI/Al4Ncp1aYcMdxQod0AXAmMBd7fmwNjjDGEcD/JjesG4BzgttQrlFQofb4/SBrwsro/hBA+DcwCjo0xdqZcm6TC6/M9IjPOxh4xxoWZ5x8CXkq/RKUtJOMoSYUTQngeeCPGePRO9nmYpL9bJbASODfG+OcQwu4kN69q4FngrBhjex7KlpQHWd4f/hn4Msn/2jQBd8QYnRFBGiCyvD9sIZlJqWsgwT/EGL+d65ol5U9f7xHAPcDDwEiSbvDPAZ/rGmRU/ZfhhiRJkiRJKmoOKCpJkiRJkoqaY26o3wgh7A/8ZrvN7THGwwpRj6T+w/uDpB3x/iBpZ7xHDB52S5EkSZIkSUXNbimSJEmSJKmoGW5IkiRJkqSiZrghSZIkSZKKmuGGJEmSJEkqaoYbkiRJkiSpqBluSJIkSZKkoma4IUmSJEmSiprhhiRJkiRJKmqGG5IkSZIkqagZbkiSJEmSpKJWWugCCmXs2LFx8uTJhS5DGrCefvrpN2KMtYWuQ5IkSdLAN2jDjcmTJzNnzpxClyENWCGEvxe6BkmSJEmDg91SJEmSJElSUTPckCRJkiRJRc1wQ5IkSZIkFbVBO+aGlEubN2+moaGBtra2QpeScxUVFdTX11NWVlboUiRJkiQNUoYbUg40NDRQVVXF5MmTCSEUupyciTGycuVKGhoamDJlSqHLkSRJkjRI2S0l1+64A/70p0JXoTxra2ujpqZmQAcbACEEampqBkULFUmSJEn9ly03cu3rX4dVq+Ckk2CA/6GrtxrowUaXwXKdkiRJkvovW27k2pIl8Pe/w8svF7oSSZIkSZIGJMONXGpthZUrk+d33VXYWjSorFmzhp///Oe9Pu6kk05izZo1OahIkiRJknLHcCOXli5987nhhvJoR+FGR0fHTo+74447GD16dK7KkiRJkqSccMyNXFqyJFkefDA8+CBs3AjDhhW2Jg0KF198MYsWLWLatGmUlZVRWVnJ+PHjmTt3Li+88AKzZ89m6dKltLW1ceGFF3LeeecBMHnyZObMmUNLSwsnnngi733ve3n00UeZOHEit912G8P8/pUkSZLUDxlu5FJXuHHmmfCFL8ALLyRBhwaXiy6CuXPTPee0aXD55Tt8+bLLLmPevHnMnTuXBx54gH/4h39g3rx5W6drvfrqq6murmbjxo0ccsghnHLKKdTU1LzlHAsWLOD666/nyiuv5LTTTuOWW27hrLPOSvc6JEmSJCkFhhu5tHQplJQkf4gCrF5d2Ho0aB166KFbgw2An/3sZ9x6660ALF26lAULFrwt3JgyZQrTMt+7Bx98MK+99lre6pUkSZKk3jDcyKUlS2D8eKirS9ZXrSpsPSqMnbSwyJcRI0Zsff7AAw9w77338thjjzF8+HCOOuoo2tra3nZMeXn51udDhgxh48aNealVkiRJknrLAUVzackS2G03qK5O1g03lCdVVVWsX7++29fWrl3LmDFjGD58OC+99BKPP/54nquTJEmSpHQVXcuNEMJo4FfAfkAEPgW8DNwITAZeA06LMRa+D8iSJckYG2PGJOt2S1Ge1NTUcMQRR7DffvsxbNgwxo0bt/W1E044gV/+8pcccMAB7L333sycObOAlUqSJElS9oou3AB+CtwVYzw1hDAUGA58FbgvxnhZCOFi4GLgK4Usks7OZMyNj3wkmSFl2DBbbiivrrvuum63l5eXc+edd3b7Wte4GmPHjmXevHlbt3/xi19MvT5JkiRJSktRdUsJIYwEjgSuAogxbooxrgFOBq7N7HYtMLswFW6juRna25NuKZC03jDckCRJkiQpdUUVbgC7A83Ar0MIz4YQfhVCGAGMizGuAMgs6wpZJADLlyfLiROTZXW13VIkSZIkScqBYgs3SoHpwC9ijAcBrSRdUHokhHBeCGFOCGFOc3NzrmpMrF2bLEePTpbV1bbckCRJkiQpB4ot3GgAGmKMT2TWbyYJOxpDCOMBMsum7g6OMV4RY5wRY5xRW1ub20rXrUuWI0cmS8MNSZIkSZJyoqjCjRjj68DSEMLemU3HAi8AtwPnZLadA9xWgPLeqqvlxqhRydIxNyRJkiRJyolinC3lAuB3mZlSFgOfJAlpbgohnAssAT5WwPoS3bXccMwNSZIkSZJSV1QtNwBijHMzXUsOiDHOjjGujjGujDEeG2OcmlkWvolEd+HGhg3Q1la4mjRorFmzhp///Od9Ovbyyy9nw4YNKVckSZIkSblTdOFG0Vi3DsrKoLw8Wa+uTpa23lAeGG5IkiRJGkyKsVtKcVi3Lmm1EUKyPmZMsly9GsaPL1xdGhQuvvhiFi1axLRp0zjuuOOoq6vjpptuor29nY985CN861vforW1ldNOO42GhgY6Ojr4xje+QWNjI8uXL+foo49m7Nix3H///YW+FEmSJEl6R4YbudIVbnTparnhoKKDzkUXwdy56Z5z2jS4/PIdv37ZZZcxb9485s6dy913383NN9/Mk08+SYyRD3/4wzz00EM0NzczYcIE/vSnPwGwdu1aRo0axY9//GPuv/9+xo4dm27RkiRJkpQjdkvJFcMN9RN33303d999NwcddBDTp0/npZdeYsGCBey///7ce++9fOUrX+Hhhx9mVNfMPpIkSZJUZGy5kStr1745DSy8tVuKBpWdtbDIhxgjl1xyCZ/5zGfe9trTTz/NHXfcwSWXXMLxxx/Pv/7rvxagQkmSJEnKji03csWWGyqgqqoq1q9fD8CsWbO4+uqraWlpAWDZsmU0NTWxfPlyhg8fzllnncUXv/hFnnnmmbcdK0mSJEnFwJYbubJuHbzrXW+ujxwJJSWGG8qLmpoajjjiCPbbbz9OPPFEzjzzTA4//HAAKisr+e1vf8vChQv50pe+RElJCWVlZfziF78A4LzzzuPEE09k/PjxDigqSZIkqSgYbuTK9i03SkqSrimGG8qT66677i3rF1544VvW99hjD2bNmvW24y644AIuuOCCnNYmSZIkSWmyW0qubB9uQLK+bl1h6pEkSZIkaYAy3MiF9vbksX24UVUFmXEPJEmSJElSOgw3cqFrMMbtw43KSsONQSTGWOgS8mKwXKckSZKk/stwIxfWrk2W3YUbzkIxKFRUVLBy5coB/4d/jJGVK1dSUVFR6FIkSZIkDWIOKJoLXeNqjBr11u1VVbBsWf7rUd7V19fT0NBAc3NzoUvJuYqKCurr6wtdhiRJkqRBzHAjF7rCDbulDFplZWVMmTKl0GVIkiRJ0qBgt5Rc2Fm4YbcUSZIkSZJSZbiRC7bckCRJkiQpb4quW0oI4TVgPdABbIkxzgghVAM3ApOB14DTYoyrC1XjDsONqirYtCl5DB2a/7okSZIkSRqAirXlxtExxmkxxhmZ9YuB+2KMU4H7MuuFs7OWG2DrDUmSJEmSUlSs4cb2TgauzTy/FphdwFqSqWCHDIFhw9663XBDkiRJkqTUFWO4EYG7QwhPhxDOy2wbF2NcAZBZ1nV3YAjhvBDCnBDCnJxO0bluXTINbAhv3V5VlSwNNyRJkiRJSk3RjbkBHBFjXB5CqAPuCSG81NMDY4xXAFcAzJgxI+aqQNavf7OVxra6tjljiiRJkiRJqSm6lhsxxuWZZRNwK3Ao0BhCGA+QWTYVrkKSlhldrTS2ZbcUSZIkSZJSV1ThRghhRAihqus5cDwwD7gdOCez2znAbYWpMKOlBUaMePt2u6VIkiRJkpS6YuuWMg64NSRjWZQC18UY7wohPAXcFEI4F1gCfKyANUJrq91SJEmSJEnKk6IKN2KMi4EDu9m+Ejg2/xXtQEsLVFezaRP8x39AUxO85z3wsffaLUWSJEmSpLQVVbhRNDItN+69F776VSgpgeuvh48ttFuKJEmSJElpK6oxN4pGSwtUVjJ3brJ66aXQ2Agr1g5/83VJkiRJkpQKw41caG2FESN49lnYYw846qhk8zNzS5KBRh1zQ5IkSZKk1BhupC3Gt7TcmDYNDsyMEvLssySDitpyQ5IkSZKk1BhupK2tDWJkfekYFi5Mwo2RI2HPPTPhRlWV4YYkSZIkSSky3EhbJrh4bs0kAA46iK3LrS037JYiSZIkSVJqDDfSlgk35jZPBJKWGwDTp8Orr8Lqil1suSFJkiRJUooMN9LW2grA3BV1jB0LEyYkm7tacMztPNBwQ5IkSZKkFBlupC0TXMxbVs0BB0AIyeb990+WL27e024pkiRJkiSlyHAjbZmWG0tXDmPSpDc3jxsHQ4bAsg67pUiSJEmSlCbDjbS1tLCFIby+aigTJ765ecgQGD8elm2uNdyQJEmSJClFhhtpa22lkXF0doa3hBsA9fXQsLEm6ZYSY2HqkyRJkiRpgDHcSFtLC8tIUo3tw42JE2FZ62jo6ID29gIUJ0mSJEnSwGO4kbbW1p2HG+tHJit2TZEkSZIkKRWGG2l7h5Yb69uGso4qZ0yRJEmSJCklhhtpa21lecmulJVBbe1bX6qvT5bLmGjLDUmSJEmSUlKU4UYIYUgI4dkQwh8z61NCCE+EEBaEEG4MIQwtWHEtLSwrncT48VCy3Ve3qyWH4YYkSZIkSekpynADuBB4cZv1HwA/iTFOBVYD5xakKkjCjVD/ti4p8Ga40UC93VIkSZIkSUpJ0YUbIYR64B+AX2XWA3AMcHNml2uB2YWpjmRA0Th+p+GGLTckSZIkSUpP0YUbwOXAl4HOzHoNsCbGuCWz3gB0Ey1ACOG8EMKcEMKc5ubm3FTX0sKyLeO6DTeGDYPq0R2GG5IkSZIkpaiowo0QwgeBphjj09tu7mbX2N3xMcYrYowzYowzarcf7TMl69d2sr6zkgkTun994viYhBt2S5EkSZIkKRWlhS6gl44APhxCOAmoAEaStOQYHUIozbTeqAeWF6rAZWtGAG+fBrbLxPpAw4v10PJSHquSJEmSJGngKqqWGzHGS2KM9THGycDpwF9ijP8LuB84NbPbOcBtBSqRZeurgJ2EG7uW2C1FkiRJkqQUFVW4sRNfAT4fQlhIMgbHVYUqZEXLSIAddksZPyHQTC0daw03JEmSJElKQ7F1S9kqxvgA8EDm+WLg0ELW06W5LWm5saMhPWproZMhrFoZyc2oH5IkSZIkDS4DpeVG/xAjze0jKS3pYPTo7nepq0uWzauG5K8uSZIkSZIGMMONNLW30xxrGDtiI6G7OVx4M9xoWjM0f3VJkiRJkjSAGW6kqbWVZmqprWzb4S5bw4215XkqSpIkSZKkgc1wI00tLUm4MWrTDnfpGoujaf2wPBUlSZIkSdLAZriRpq6WG2O27HCXmhoIdNK0oTKPhUmSJEmSNHAZbqSpq+VGTecOdykthZqKVprbDDckSZIkSUqD4UaKNq/dwBrG7HAa2C51w1tp2rSD6VQkSZIkSVKvGG6k6I3lyVgbdeN2MFVKRm3lRpq2VEOM+ShLkiRJkqQBzXAjRc2vdwBQO750p/vVjWqniTrYsCEfZUmSJEmSNKAZbqSouSlpiVE7oWyn+9WN2UwztdDSko+yJEmSJEka0Aw3UtTcnCxr68t3ul9dTQerqWbTKsMNSZIkSZKyZbiRoqZVQwCo3W3YTverq0taeLyxdGPOa5IkSZIkaaAz3EhR8+oySuigepehO92vti75sjct25yPsiRJkiRJGtAMN1LUvG4oNWEVJe/wVa0bn7TwaF6xJQ9VSZIkSZI0sBlupKh5fQW1Q1a943519UnLjqbXO3NdkiRJkiRJA57hRoqaW0dQW7b2Hfer260CgKbmXFckSZIkSdLAV1ThRgihIoTwZAjhuRDC/BDCtzLbp4QQngghLAgh3BhC2PmgFznSvLGS2vJ177jf6AnDKWUzTW8MyUNVkiRJkiQNbEUVbgDtwDExxgOBacAJIYSZwA+An8QYpwKrgXMLUVxz+0hqh7/z9K6hqpJammlaXZqHqiRJkiRJGtiKKtyIia70oCzziMAxwM2Z7dcCs/NdW0cHrNpSRe3wDe+8c3k5dTTTvLY894VJkiRJkjTAFVW4ARBCGBJCmAs0AfcAi4A1McauqUcagIk7OPa8EMKcEMKc5uZ0B7xYuRIiJdRWtb3zziFQV7qSpvXDUq1BkiRJkqTBqOjCjRhjR4xxGlAPHAq8q7vddnDsFTHGGTHGGbW1tanW1ZWV1I5s79H+dUPX0NQ6PNUaJEmSJEkajIou3OgSY1wDPADMBEaHELoGsKgHlue7nq3hxpgtO98xo7ZiPU0bR+awIkmSJEmSBoeiCjdCCLUhhNGZ58OADwAvAvcDp2Z2Owe4Ld+1bQ03ajp7tH/dsBZaOyrY0IMhOiRJkiRJ0o4VVbgBjAfuDyH8DXgKuCfG+EfgK8DnQwgLgRrgqnwX1rwiabFRO7bbHjFvU1eZpBopD/0hSZIkSdKgU1RzkcYY/wYc1M32xSTjbxRM8/LNQCk1dUN6tH/dqGRsjqYmmDQph4VJkiRJkjTAFVvLjX6r+fUtjGEVZSN7NgNK7ejNQBJuSJIkSZKkvjPcSElzY6SWZqis7NH+ddVJNxa7pUiSJEmSlB3DjZQ0v0ESbowY0aP962qTsTlsuSFJkiRJUnYMN1LSvLKkVy03RowZyjA20PR6z2ZXkSRJkiRJ3TPcSEnz6tJehRuhqpJammla0ZHjyiRJkiRJGtgMN1LQ2QlvrBvaq24pVFZSRxNNrxtuSJIkSZKUDcONFKxZAx2dveuWQlUVdTQ5oKgkSZIkSVky3EhBV0DRp5Ybb/gRSJIkSZKUDf+yTkFW4caqUmLMXW2SJEmSJA10hhsp2BpulK6BioqeHVRVRS3NbNpcwrp1uatNkiRJkqSBznAjBV3hRt3Itp4flGm5se3xkiRJkiSp9ww3UtCUZBSMHb2l5wdtE250HS9JkiRJknrPcCMFjY0wurSF8tHDen5QZrYUMNyQJEmSJCkbhhspaGyEcWUrYdSonh80YkQyACmGG5IkSZIkZcNwIwVNTTCupLl34cbQodSWrd16vCRJkiRJ6hvDjRQ0NkJdZ2Pvwg2gYuRQRg7d6ICikiRJkiRloajCjRDCriGE+0MIL4YQ5ocQLsxsrw4h3BNCWJBZjslnXY2NMK5jGYwc2bsDKyupK19ryw1JkiRJkrJQVOEGsAX4QozxXcBM4P+EEN4NXAzcF2OcCtyXWc+LTZtg9WoYt6mh1y03qKyktmyN4YYkSZIkSVkoqnAjxrgixvhM5vl64EVgInAycG1mt2uB2fmqqSuYGMfrfQo36oasMtyQJEmSJCkLRRVubCuEMBk4CHgCGBdjXAFJAALU7eCY80IIc0IIc5pTGuiiK5ioo6n34UZVFXWh2TE3JEmSJEnKQlGGGyGESuAW4KIY47qeHhdjvCLGOCPGOKO2tjaVWhobk+U4ej+gKJWV1MVGmpuhszOVciRJkiRJGnSKLtwIIZSRBBu/izH+IbO5MYQwPvP6eCBvHT3eEm70ZUDRjhV0dsKqVenXJkmSJEnSYFBU4UYIIQBXAS/GGH+8zUu3A+dknp8D3JavmrJquTFqFLXtSwEcd0OSJEmSpD4qqnADOAI4GzgmhDA38zgJuAw4LoSwADgus54XTU0wvHwLI9jQ+3Cjupq61tcAHHdDkiRJkqQ+Ki10Ab0RY3wECDt4+dh81tKlsRHGVW2AdvoWbpA0/bDlhiRJkiRJfVNsLTf6ncZGGDe8JVnpU7iRpBqGG5IkSZIk9Y3hRpYaG2FcxRooKYERI3p3cHU1NawEDDckSZIkSeorw40sNTZCXenqZKaUsKMeMztQXU0pHdSM3LR1YFJJkiRJktQ7hhtZ6OiAN96AcUPe6H2XFIDqagAmjm5l+fKUi5MkSZIkaZAw3MjC669DZyfUlyzLKtyor1xLQ0PKxUmSJEmSNEgYbmRh6dJkWd+5NLtwY9hKww1JkiRJkvrIcCMLXYFE/abFfQs3Skth5Ejqyxppboa2tnTrkyRJkiRpMDDcyMLWcKNtYTKgaF9UV1MflgE47oYkSZIkSX1guJGFhgaoqIDqda/B6NF9O0l1Nbt2vrb1fErRypWwaBG0tha6EkmSJElSDhluZKGhAerrI2H1Kqit7dtJqqupb18MvDmGh7IQI/z3f7PyoA9w59izuHXPL/LC6PcQj/0A/PWvha5OkiRJkpQDhhtZaGiA+rrNyUpdXd9OUl3NxNZXtp5PWVi5kr+//x859ZzhjH/uTk7iTj7Krey75Tn2fvAK/vDe/yCefwFs2VLoSiVJkiRJKTLcyEJDA+xaneny0Ndwo6aGqjVLGTXKcCMrS5dy3f7fZ7+Hf86fyz/MhZ8v5cEH4emn4Ze/hIp9JnMKf+D0/3ovbR/6GGzaVOiKJUmSJEkpMdzoo85OWLYM6qvWJhuy6JbCqlXU10fDjb564w1+cvBv+F8r/p3pB3Yw7+Wh/OjfA0ceCdOnw2c+A8/MLeF734Ob+Diz7rqI9Wd9LvkQJUmSJElFz3Cjj5qakt4N9RXNyYYsuqXQ0UH9LlsMN/qivZ2fH3oNn2/+Kqcc9QZ3PzGaSZPevltpKXz1q3D99fDXkvdx6u9PY/N3Lst/vZIkSZKk1Blu9NHWaWCHvJ48ySbcAOpr2gw3+uC2j1/H+a9+ng8dvJwb7hlLefnO9z/9dLjyysDdzOJzl46DBx/MT6GSJEmSpJwx3OijreFG5xIYMgTGjOnbibrCjVHraWx0KIjeWPTrhzj7tlOYUbeEGx6aQGlpz4775KcCX/tiO1dxLr/76C3Q0pLbQiVJkiRJOVVU4UYI4eoQQlMIYd4226pDCPeEEBZkln1MGXpna7ixaXEy3kZJH7+UXeFG5RpihOXLUypwgGtf2sTHzxvFkJLI7x/aheHDe3f8pd8v530HruOzq77Hwgt+mpsiJUmSJEl5UVThBnANcMJ22y4G7osxTgXuy6zn3NKlMHQojF3/at8HEwUYOxaAPYYlqcaiRWlUN8B1dnLxkX/l6S0H8usfr0q71pMAABEHSURBVGHS3hW9PkVpKfz29pGUlZdwxjXHs+nROTkoVJIkSZKUD0UVbsQYHwJWbbf5ZODazPNrgdn5qGXBAth9dyhpbuz7eBsAEycCMDW+AsArr6RR3cB2+3l/5PLXPsI/H/Ucsy/sZvTQHtptN7jqVzCHQ/ja7PmweXOKVUqSJEmS8qWowo0dGBdjXAGQWe4waQghnBdCmBNCmNPc3JzVm77yCuy1F8m0KdmEG1VVMGoUE9a/zPDhhhvvZMkd8/jEVe9l+qiF/PDOA7I+30fOGsHnZi3m35vP4f7zb0mhQkmSJElSvg2EcKPHYoxXxBhnxBhn1GbRlaSjAxYuhL33JvtwA6C+npJlS5k61XBjZzavaeWMUzaxJZRx493VlFeEVM77o1t2Z+qIZXziyvew9vklqZxTkiRJkpQ/AyHcaAwhjAfILJty/YZLlkB7O+w1ZTOsW5fdmBsA9fWwdCl77ZV0d1H3vnn0gzzaNp0rvvp39jy0OrXzjhgBv/ltCQ1xIhd90A9AkiRJkorNQAg3bgfOyTw/B7gt12/Y1bpir9rVyZNsW27suis0NLDXXrB4sUM/dOfPX3+Y7889iX868AlO/+5+qZ//sNnj+eoxT3DNkmP5n689lfr5JUmSJEm5U1ThRgjheuAxYO8QQkMI4VzgMuC4EMIC4LjMek5tDTdGvp48SaFbCo2N7LX7Fjo64NVXszvdQNPwxDLO/rd92G/YQi5/cHrO3ucbt83goIoXOO+yKTS9tiFn7yNJkiRJSldRhRsxxjNijONjjGUxxvoY41UxxpUxxmNjjFMzy+1nU0ndyy/DyJEwriOZvjWVcCNG9hqTDHLquBtvat/QwanHraEtlvP7/xnK8FFlOXuvoZVD+c0vWlnXWck/HfcqMebsrSRJkiRJKSqqcKO/6JopJazIhBu77JLdCXfdFYC9yv++9fxKXHTkMzyxfl+uueAZ9jl+t5y/376fOIR/O+x2bl+4L1ddsjDn7ydJkiRJyp7hRh9snQZ2wQIoK9saTvRZfT0A1WtfpabGcKPLNV94nl8+fQhf3vdPfPRnR+XtfS/684kcW/EI5/9wV+Y80JK395UkSZIk9Y3hRi9t3JjMlrI13Nh9dygtze6kXeFIQwP77APPP591mUXvoRtX8NkfT+XoEU/yvUePzut7l4yq4vqbShkXX+ejJ22kucn+KZIkSZLUnxlu9NJzz0GMsN9+JOHG1KnZn7SqKhnEY+lSDjsMnnkGNm3K/rTF6rkn2/nQmVVMKfk7N91fS+nI4XmvofZDM/nDhQ/RtLGK02e+ypYteS9BkiRJktRDhhu99NhjyfLwmREWLkwn3ICka0pDAzNnQlsb/O1v6Zy22Cxe2Mms929kZOdq7r5yCWMPmVKwWg7+yVn88ojf8pdXd+fzH3jOAUYlSZIkqZ8y3Oilxx6DSZNgAsthw4b0wo3Jk2HhQmbOfPN9BpulSyLHz1jF5rYO7v6Xu9j1U8cVtqAQ+MQDn+Bfdr+N/3zwQL7zsecKW48kSZIkqVuGG7302GNw+OEkXVIgvXDjoIPghReor9nIhAnw+OPpnLZYvPxS5Ij91tK8tow7PnYN7/qPTxe6pERpKf/+/CzO2eUuvnnLgfz0k88WuiJJkiRJ0nYMN3ph6VJoaMiEG11TmqQVbkyfDh0dhHnPM3Pm4Ao3/nJPB4dP20Db+k08cOp/cdgN/wIhFLqsrUqGV/Cr+e/hozUPcNE1B3HVJx8udEmSJEmSpG0YbvTC1vE2ulpulJdnPw1sl+nTk+UzzzBzJixeDE1N6Zy6v+rogO9/vZXjZ8H49td4/DPXcNBNl0BJ//u2LK0eyXULDuX4mjl8+pr38ePZDxW6JEmSJElSRv/7K7Ife/RRGDYMpk0jCTf22CO9P8QnTYLqanjmGY44Itl0333pnLo/euklOObAlXz1eyM4JfyBx342h91/+eV+1WJje+VjhnP74v05deKjfOG2I/ny+x4ldjrKqCRJkiQVmuFGD3V2wq23wvvfD2WlEZ56KjMfbEpCSFpvZFpuTJwI11+f3un7i+Zm+JdPr+eAd2/m+fmBX0/4Gjc8uQcjLzin0KX1SPnIcm5YdCif2+s+fvTIe/jH/Z9h4wYDDkmSJEkqJMONHnr4YViyBM4+G5g3D5Yvh1mz0n2T6dPh+ecp2bKJM86AO++ElSvTfYtCefpp+NQpa9lt/CZ+dtVw/rHkd7z09d/xiVe/STh4eqHL65Uh5aX81wtH853D/h+/feFgZu7awIIXtxS6LEmSJEkatAw3eui//xsqK2H2bJLUAdIPN2bMgE2b4KmnOPNM2LIFbrkl3bfIlxiTDOjfvrKGGZOamDEDbvrDEM6J1zL/1Ev51cKjqPvOBTB0aKFL7ZMwpISvP/ZB7jjtGhpWDefg/Tdx0zUbCl2WJEmSJA1KIcbB2aR+xowZcc6cOT3ad8MG2GUXOPVUuPpq4Jhj4I034G9/S7eodetg/Hg44wzilb/i3e+Gqqpk5pR+OMbmW8QIC+e38/AtTTx0TxsPPjeG11rGAnAIT3JWzV2c8+kyRn3+XKirK3C16Vpy2e/4+CV78DgzOfUDq/nptWOYMKHQVRVeCOHpGOOMQtchSZIkaeAz3OiBb3wDvvtdeOQROOKA9VBTAxddBD/8YfqFnXsu3HgjrFjB1b+v4txzk0Dlk59M/636asuaFhbe+xrP3reKZ56BpxeP5plVk1nbORKAsTTz3rInOHGvxXzw5CFMOOsY2Geffj1YaLY23/8IPzr5Eb69/iJKSkv4/EWRi75Sztixha6scAw3JEmSJOWL4cY7eO65pLfImWfCtdcC3/oWXHop/PWv8J73pF/Y448nc83+4hd0nvdZjjwymVnkhRfy3+ChvXENr/1lMS88sor5czczf/Ew5q8cx8ubd2cT5QCU08YBwxYwffzrHPzujbz36FL2OWEKYZ+9+39zk7StXMniz/6Qr908jRs4g2Glmzh9djun/1MVxxwDpaWFLjC/DDckSZIk5cuACTdCCCcAPwWGAL+KMV62s/17Em48/zycfDK0tibhQs0bL8MBB8App8B116VX/LZihMMOg5dfhkce4Xn255BDkpli//Qn2HPPdN9u7VpYNGc1ix5ZzqJn17NoQSeLVgxj0bpalnZMIG4zLMuksuXsW9vIvru3se9BQznw6Gr2nVVP2fCydIsqdnPm8MKXfs2PH5jOTXyM9YxkbEULJ81cxeFHV3D4B2vY78AhDBlS6EJzy3BDkiRJUr4MiHAjhDAEeAU4DmgAngLOiDG+sKNj3inc+POf4aMfhVGjkilgDzuMJOl46CF48cVkEI5cWboUZs5MRhS9+GIeGXcKs/95V1pb4YwzAieemPTyqKqC4RWdVJR1sKW9g81tHWxu72RzeycbWjpZuyaydnUna1/fyNrGNlY0dLB8RWDZ8sDyN4aytGU0K7eMfstb14Zm9hj+OnuMW88eUyJ7HDCcfd5Xx7uP2YXKUQP8r/G0LVlC2+9u4c6b1nPT8/vwl47308Q4AMpDO3uObGKvurXsXt/OuIml1E0axrhdy6mbWMbI2nKGVQ9j2OhyKoaXUDEs5KYhTGtrMpXN8OFJE6UdiBEaG5OQb9Ik2GOPdz614YYkSZKkfBko4cbhwKUxxlmZ9UsAYozf39Ex7xRuLF0KF1wAv/hFMsYnkEz/On8+HHdcitXvwPz58LnPJXPQAq8ymR/wFX7D2WxgRJ9OGeikjiYmsJyJw1ZTX7OB3es3s8c+ZexxaA27H7UbVftMHNBjYxRMWxtx7nMsfqiBxx7cxN9eKuOV10fxyoZ6XmUybQx7x1OU08ZQNlFCJ4FISYjJks5tnkdCSJZd+0UCZJZdD2ISWMSYeb2igji6OrMteUCy7OxMxrrdkpnt9rvfha997Z0v2XBDkiRJUr4MlHDjVOCEGOOnM+tnA4fFGM/fbr/zgPMyq3sDL+ehvLHAG3l4n/5ksF2z19u9STHG2lwXI0mSJEkDZYjD7poavC21iTFeAVyR+3LeFEKYM9j+93qwXbPXK0mSJEmFNVCms2gAdt1mvR5YXqBaJEmSJElSHg2UcOMpYGoIYUoIYShwOnB7gWuSJEmSJEl5MCC6pcQYt4QQzgf+TDIV7NUxxvkFLqtLXrvB9BOD7Zq9XkmSJEkqoAExoKgkSZIkSRq8Bkq3FEmSJEmSNEgZbkiSJEmSpKJmuJGFEMIJIYSXQwgLQwgXd/N6eQjhxszrT4QQJm/z2iWZ7S+HEGbls+6+6uv1hhAmhxA2hhDmZh6/zHftfdGD6z0yhPBMCGFLCOHU7V47J4SwIPM4J39V912W19uxzefrYL6SJEmS8mpADChaCCGEIcB/AceRTEX7VAjh9hjjC9vsdi6wOsa4ZwjhdOAHwMdDCO8mmdFlX2ACcG8IYa8YY0d+r6LnsrnezGuLYozT8lp0Fnp4vUuATwBf3O7YauCbwAwgAk9njl2dj9r7IpvrzdhYTJ+vJEmSpIHFlht9dyiwMMa4OMa4CbgBOHm7fU4Grs08vxk4NoQQMttviDG2xxhfBRZmztefZXO9xegdrzfG+FqM8W9A53bHzgLuiTGuygQa9wAn5KPoLGRzvZIkSZJUUIYbfTcRWLrNekNmW7f7xBi3AGuBmh4e299kc70AU0IIz4YQHgwhvC/XxaYgm89ooH6+O1MRQpgTQng8hDA73dIkSZIkaefsltJ33bVI2H5e3R3t05Nj+5tsrncFsFuMcWUI4WDgf0II+8YY16VdZIqy+YwG6ue7M7vFGJeHEHYH/hJCeD7GuCil2iRJkiRpp2y50XcNwK7brNcDy3e0TwihFBgFrOrhsf1Nn6830/1mJUCM8WlgEbBXzivOTjaf0UD9fHcoxrg8s1wMPAAclGZxkiRJkrQzhht99xQwNYQwJYQwlGSA0O1nibgd6Jop41TgLzHGmNl+emZ2kSnAVODJPNXdV32+3hBCbWbASjL/sz8VWJynuvuqJ9e7I38Gjg8hjAkhjAGOz2zrz/p8vZnrLM88HwscAbyw86MkSZIkKT12S+mjGOOWEML5JH+0DgGujjHODyF8G5gTY7wduAr4TQhhIUmLjdMzx84PIdxE8gfgFuD/9OeZUiC76wWOBL4dQtgCdACfjTGuyv9V9FxPrjeEcAhwKzAG+FAI4Vsxxn1jjKtCCN8hCQwAvj2Qrxd4F/B/QwidJIHpZdvNsiJJkiRJORWShgSSJEmSJEnFyW4pkiRJkiSpqBluSJIkSZKkoma4IUmSJEmSiprhhiRJkiRJKmqGG5IkSZIkqagZbkiSJEmSpKJmuKGiEEK4K4SwJoTwxx28/p8hhJZ81yVJkiRJKjzDDRWLHwFnd/dCCGEGMDq/5UiSJEmS+gvDDRVMCOEHIYT/vc36pSGEL3S3b4zxPmB9N+cYQhJ8fDlnhUqSJEmS+jXDDRXSDcDHt1k/Dfh9L89xPnB7jHFFalVJkiRJkopKaaEL0OAVY3w2hFAXQpgA1AKrY4xLenp85riPAUflqERJkiRJUhEw3FCh3QycCuxC0pKjNw4C9gQWhhAAhocQFsYY90y3REmSJElSf2a4oUK7AbgSGAu8vzcHxhj/RBKKABBCaDHYkCRJkqTBxzE3VFAxxvlAFbBsZ+NmhBAeJhmP49gQQkMIYVa+apQkSZIk9W8hxljoGiRJkiRJkvrMlhuSJEmSJKmoOeaG+o0Qwv7Ab7bb3B5jPKwQ9UiSJEmSioPdUiRJkiRJUlGzW4okSZIkSSpqhhuSJEmSJKmoGW5IkiRJkqSiZrghSZIkSZKK2v8P2fAmvoDhvfkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x1080 with 17 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 15))\n",
    "i = 1\n",
    "for col in numeric_features:\n",
    "    plt.subplot(5, 4, i)\n",
    "    i += 1\n",
    "    sns.distplot(Train_data[col], label='train', color='r', hist=False)\n",
    "    sns.distplot(TestA_data[col], label='test', color='b', hist=False)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 训练集和测试集放在一起，方便构造特征\n",
    "Train_data['train']=1\n",
    "TestA_data['train']=0\n",
    "\n",
    "data = pd.concat([Train_data, TestA_data], ignore_index=True, sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['used_time'] = (pd.to_datetime(data['creatDate'], format='%Y%m%d', errors='coerce') - \n",
    "                            pd.to_datetime(data['regDate'], format='%Y%m%d', errors='coerce')).dt.days\n",
    "\n",
    "# 看一下空数据，有 15k 个样本的时间是有问题的，我们可以选择删除，也可以选择放着（不管或者作为一个单独类别）。\n",
    "# XGBoost 之类的决策树，其本身就能处理缺失值，所以可以不用管；\n",
    "data['used_time'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 由于power 特征的分布特性，这里对于其进行log变化\n",
    "data['power'] = np.log1p(data['power'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting is completed!\n",
      "*\n",
      "p is done\n"
     ]
    }
   ],
   "source": [
    "#聚类类别特征\n",
    "from sklearn.cluster import KMeans\n",
    "kms = KMeans(n_clusters=5)\n",
    "k_t = kms.fit_predict(data[numeric_features])\n",
    "d = k_t.tolist()\n",
    "data['kmeans_type'] = d\n",
    "import pickle\n",
    "from sklearn.decomposition import LatentDirichletAllocation, NMF, TruncatedSVD,PCA \n",
    "\n",
    "seed,n_comp = 2021,8 \n",
    "fitting = True \n",
    "if fitting: \n",
    "    svd = TruncatedSVD(random_state=seed,n_components=n_comp).fit_transform(data[numeric_features]) \n",
    "    pca = PCA(n_components=n_comp, random_state=seed).fit(data[numeric_features]) \n",
    "#     umap = UMAP(n_components=n_comp, random_state=seed).fit(data[numeric_features]) \n",
    "    f = open('./input/svd','wb')\n",
    "    pickle.dump(svd,f)\n",
    "    f.close()\n",
    "\n",
    "data[[f'svd_{i + 1}' for i in range(n_comp)]]= pd.DataFrame(svd)#,index=df.index \n",
    "print(\"*\") \n",
    "p = pca.transform(data[numeric_features]) \n",
    "# u = umap.transform(df[num_cols]) \n",
    "data[[f'pca_{i + 1}' for i in range(n_comp)]]= pd.DataFrame(p) \n",
    "print('p is done') \n",
    "# df[[f'umap_{i + 1}' for i in range(n_comp)]]= pd.DataFrame(u) \n",
    "del p#,u"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 交叉特征组合列表\n",
    "ls =['bodyType','fuelType','offerType','regionCode','model','brand',\n",
    "            'seller']\n",
    "pair = []\n",
    "seen = set()\n",
    "for f1 in ls:\n",
    "    for f2 in ls:\n",
    "        if ((f1,f2) not in seen) and ((f1,f2) not in seen) and f1!=f2:\n",
    "            pair.append([f1,f2])\n",
    "            seen.add((f1,f2))\n",
    "del ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算某品牌的销售统计量，这种特征不能细粒度的统计，防止发生信息过多的泄露\n",
    "train_gb = Train_data.groupby(\"brand\")\n",
    "all_info = {}\n",
    "for kind, kind_data in train_gb:\n",
    "    info = {}\n",
    "    kind_data = kind_data[kind_data['price'] > 0]\n",
    "    info['brand_amount'] = len(kind_data)\n",
    "    info['brand_price_max'] = kind_data.price.max()\n",
    "    info['brand_price_median'] = kind_data.price.median()\n",
    "    info['brand_price_min'] = kind_data.price.min()\n",
    "    info['brand_price_sum'] = kind_data.price.sum()\n",
    "    info['brand_price_std'] = kind_data.price.std()\n",
    "    info['brand_price_average'] = round(kind_data.price.sum() / (len(kind_data) + 1), 2)\n",
    "    all_info[kind] = info\n",
    "brand_fe = pd.DataFrame(all_info).T.reset_index().rename(columns={\"index\": \"brand\"})\n",
    "data = data.merge(brand_fe, how='left', on='brand')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
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     ]
    }
   ],
   "source": [
    "## 这部分就参考天才的代码了\n",
    "from scipy.stats import entropy\n",
    "\n",
    "feat_cols = []\n",
    "def genPower(df,col='kilometer'):\n",
    "    df[col+\"_genFeat1\"]=(df['kilometer'] > 5).astype(int)\n",
    "    df[col+\"_genFeat2\"]=(df['kilometer']> 10).astype(int)\n",
    "    df[col+\"_genFeat3\"]=(df['kilometer']> 15).astype(int)\n",
    "    return df\n",
    "data = genPower(data)\n",
    "\n",
    "### count编码\n",
    "for f in tqdm(['regDate_year','model', 'brand', 'regionCode']): #kmeans_type,pca,svd\n",
    "    data[f + '_count'] = data[f].map(data[f].value_counts())\n",
    "    feat_cols.append(f + '_count')\n",
    "\n",
    "### 用数值特征对类别特征做统计刻画，随便挑了几个跟price相关性最高的匿名特征\n",
    "for f1 in tqdm(['model', 'brand', 'regionCode']):\n",
    "    group = data.groupby(f1, as_index=False)\n",
    "    for f2 in tqdm(['v_0', 'v_3', 'v_8', 'v_12']):\n",
    "        feat = group[f2].agg({\n",
    "            '{}_{}_max'.format(f1, f2): 'max', '{}_{}_min'.format(f1, f2): 'min',\n",
    "            '{}_{}_median'.format(f1, f2): 'median', '{}_{}_mean'.format(f1, f2): 'mean',\n",
    "            '{}_{}_std'.format(f1, f2): 'std', '{}_{}_mad'.format(f1, f2): 'mad'\n",
    "        })\n",
    "        data = data.merge(feat, on=f1, how='left')\n",
    "        feat_list = list(feat)\n",
    "        feat_list.remove(f1)\n",
    "        feat_cols.extend(feat_list)\n",
    "for f1 in tqdm(['v_0', 'v_3', 'v_8', 'v_12']):\n",
    "    data[f'{f1}_squared'] = data[f1]**2\n",
    "    data[f'{f1}_log'] = np.log1p(data[f1])\n",
    "    for f2 in tqdm(['v_0', 'v_3', 'v_8', 'v_12']):\n",
    "        if f1 != f2:\n",
    "            data[f'{f1}_{f2}_add']  = data[f1]+data[f2]\n",
    "            data[f'{f1}_{f2}_minus']  = data[f1]-data[f2]\n",
    "            data[f'{f1}_{f2}_multi']  = data[f1]*data[f2]\n",
    "            data[f'{f1}_{f2}_div']  = data[f1]/(data[f2]+0.0000001)\n",
    "\n",
    "### 类别特征的二阶交叉\n",
    "for f_pair in tqdm([['model', 'brand'], ['model', 'regionCode'], ['brand', 'regionCode']]):#+pair\n",
    "    ### 共现次数\n",
    "    data['_'.join(f_pair) + '_count'] = data.groupby(f_pair)['SaleID'].transform('count')\n",
    "    ### nunique、熵\n",
    "    data = data.merge(data.groupby(f_pair[0], as_index=False)[f_pair[1]].agg({\n",
    "        '{}_{}_nunique'.format(f_pair[0], f_pair[1]): 'nunique',\n",
    "        '{}_{}_ent'.format(f_pair[0], f_pair[1]): lambda x: entropy(x.value_counts() / x.shape[0])\n",
    "    }), on=f_pair[0], how='left')\n",
    "    data = data.merge(data.groupby(f_pair[1], as_index=False)[f_pair[0]].agg({\n",
    "        '{}_{}_nunique'.format(f_pair[1], f_pair[0]): 'nunique',\n",
    "        '{}_{}_ent'.format(f_pair[1], f_pair[0]): lambda x: entropy(x.value_counts() / x.shape[0])\n",
    "    }), on=f_pair[1], how='left')\n",
    "    ### 比例偏好\n",
    "    data['{}_in_{}_prop'.format(f_pair[0], f_pair[1])] = data['_'.join(f_pair) + '_count'] / data[f_pair[1] + '_count']\n",
    "    data['{}_in_{}_prop'.format(f_pair[1], f_pair[0])] = data['_'.join(f_pair) + '_count'] / data[f_pair[0] + '_count']\n",
    "    \n",
    "    feat_cols.extend([\n",
    "        '_'.join(f_pair) + '_count',\n",
    "        '{}_{}_nunique'.format(f_pair[0], f_pair[1]), '{}_{}_ent'.format(f_pair[0], f_pair[1]),\n",
    "        '{}_{}_nunique'.format(f_pair[1], f_pair[0]), '{}_{}_ent'.format(f_pair[1], f_pair[0]),\n",
    "        '{}_in_{}_prop'.format(f_pair[0], f_pair[1]), '{}_in_{}_prop'.format(f_pair[1], f_pair[0])\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 42/42 [27:54<00:00, 39.88s/it]\n"
     ]
    }
   ],
   "source": [
    "#生成交叉列表\n",
    "for f_pair in tqdm(pair[:]):#pair[:10]已经足够提分\n",
    "    ### 共现次数\n",
    "    data['_'.join(f_pair) + '_count'] = data.groupby(f_pair)['SaleID'].transform('count')\n",
    "    data = data.merge(data.groupby(f_pair[0], as_index=False)[f_pair[1]].agg({\n",
    "        '{}_{}_nunique'.format(f_pair[0], f_pair[1]): 'nunique',\n",
    "        '{}_{}_ent'.format(f_pair[0], f_pair[1]): lambda x: entropy(x.value_counts() / x.shape[0])\n",
    "    }), on=f_pair[0], how='left')\n",
    "    data = data.merge(data.groupby(f_pair[1], as_index=False)[f_pair[0]].agg({\n",
    "        '{}_{}_nunique'.format(f_pair[1], f_pair[0]): 'nunique',\n",
    "        '{}_{}_ent'.format(f_pair[1], f_pair[0]): lambda x: entropy(x.value_counts() / x.shape[0])\n",
    "    }), on=f_pair[1], how='left')\n",
    "    feat_cols.extend([\n",
    "        '_'.join(f_pair) + '_count',\n",
    "        '{}_{}_nunique'.format(f_pair[0], f_pair[1]), '{}_{}_ent'.format(f_pair[0], f_pair[1]),\n",
    "        '{}_{}_nunique'.format(f_pair[1], f_pair[0]), '{}_{}_ent'.format(f_pair[1], f_pair[0]),\n",
    "        '{}_in_{}_prop'.format(f_pair[0], f_pair[1]), '{}_in_{}_prop'.format(f_pair[1], f_pair[0])\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X train shape: (250000, 649)\n",
      "X test shape: (50000, 649)\n"
     ]
    }
   ],
   "source": [
    "#标识是否训练集\n",
    "import pickle\n",
    "import io\n",
    "train = data[data['train']==1]\n",
    "test = data[data['train']==0]\n",
    "\n",
    "## 选择特征列\n",
    "feature_cols = [col for col in data.columns if col not in ['SaleID','name','regDate','creatDate','bodyType','fuelType','offerType','regionCode','price','model','brand',\n",
    "            'seller','train']]\n",
    "#2021-04-29\n",
    "feature_cols = [col for col in data.columns if col not in ['SaleID','name','regDate','creatDate','price','train']]\n",
    "'''\n",
    "fixe here to check fitting.\n",
    "'''\n",
    "\n",
    "feature_cols = [col for col in feature_cols if col not in date_features[1:]]\n",
    "\n",
    "## 提前特征列，标签列构造训练样本和测试样本\n",
    "\n",
    "X_data = train[feature_cols]\n",
    "Y_data = train['price']\n",
    "\n",
    "X_test  = test[feature_cols]\n",
    "\n",
    "print('X train shape:',X_data.shape)\n",
    "print('X test shape:',X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_data = X_data.fillna(-1)\n",
    "X_test = X_test.fillna(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "\n",
    "def cv_predict(model,X_data,Y_data,X_test,sub):\n",
    "\n",
    "    oof_trn = np.zeros(X_data.shape[0])\n",
    "    oof_val = np.zeros(X_data.shape[0])\n",
    "    feature_importance_df = pd.DataFrame()\n",
    "    ## 5折交叉验证方式\n",
    "    kf = KFold(n_splits=5,shuffle=True,random_state=0)\n",
    "    for idx, (trn_idx,val_idx) in enumerate(kf.split(X_data,Y_data)):\n",
    "        print('--------------------- {} fold ---------------------'.format(idx))\n",
    "        trn_x, trn_y = X_data.iloc[trn_idx].values, Y_data.iloc[trn_idx]\n",
    "        val_x, val_y = X_data.iloc[val_idx].values, Y_data.iloc[val_idx]\n",
    "\n",
    "        xgr.fit(trn_x,trn_y,eval_set=[(val_x, val_y)],eval_metric='mae',verbose=30,early_stopping_rounds=20,)\n",
    "\n",
    "        oof_trn[trn_idx] = xgr.predict(trn_x)\n",
    "        oof_val[val_idx] = xgr.predict(val_x)\n",
    "        sub['price'] += xgr.predict(X_test.values) / kf.n_splits\n",
    "\n",
    "        pred_trn_xgb=xgr.predict(trn_x)\n",
    "        pred_val_xgb=xgr.predict(val_x)\n",
    "        # feature importance\n",
    "        fold_importance_df = pd.DataFrame()\n",
    "        fold_importance_df[\"feature\"] = X_data.columns.to_list()\n",
    "        fold_importance_df[\"importance\"] = xgr.feature_importances_#(importance_type='gain')\n",
    "        fold_importance_df[\"fold\"] = idx\n",
    "        feature_importance_df = pd.concat([feature_importance_df, fold_importance_df], axis=0)     \n",
    "        print('trn mae:', mean_absolute_error(trn_y, oof_trn[trn_idx]))\n",
    "        print('val mae:', mean_absolute_error(val_y, oof_val[val_idx]))\n",
    "        if idx==0:\n",
    "            break\n",
    "    feature_importance_df.sort_values(by='importance',inplace=True)\n",
    "    return model,oof_trn,oof_val,sub,feature_importance_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 原始标签预测\n",
    "sub2 = test[['SaleID']].copy()\n",
    "sub2['price'] = 0\n",
    "\n",
    "oof_trn = np.zeros(train.shape[0])\n",
    "oof_val = np.zeros(train.shape[0])\n",
    "\n",
    "## xgb-Model\n",
    "xgr = xgb.XGBRegressor(n_estimators=120, learning_rate=0.1, gamma=0, subsample=0.8,\\\n",
    "        colsample_bytree=0.9, max_depth=7) #,objective ='reg:squarederror'\n",
    "\n",
    "model2,oof_trn,oof_val,sub2,feature_importance_df = cv_predict(xgr,X_data,Y_data,X_test,sub2)\n",
    "\n",
    "print('Train mae:',mean_squared_error(Y_data,oof_trn))\n",
    "print('Val mae:', mean_squared_error(Y_data,oof_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "      <th>fold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>162</td>\n",
       "      <td>regionCode_v_12_min_x</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>344</td>\n",
       "      <td>model_v_12_median_y</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>336</td>\n",
       "      <td>model_v_8_max_y</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>297</td>\n",
       "      <td>bodyType_fuelType_ent_y</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>209</td>\n",
       "      <td>v_12_squared</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>277</td>\n",
       "      <td>bodyType_model_ent_x</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>121</td>\n",
       "      <td>brand_v_0_median_x</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>gearbox</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>386</td>\n",
       "      <td>regionCode_v_8_median_y</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>324</td>\n",
       "      <td>model_v_0_max_y</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     feature  importance  fold\n",
       "162    regionCode_v_12_min_x    0.000004     0\n",
       "344      model_v_12_median_y    0.000012     0\n",
       "336          model_v_8_max_y    0.000014     0\n",
       "297  bodyType_fuelType_ent_y    0.000018     0\n",
       "209             v_12_squared    0.000020     0\n",
       "277     bodyType_model_ent_x    0.000027     0\n",
       "121       brand_v_0_median_x    0.000029     0\n",
       "4                    gearbox    0.000029     0\n",
       "386  regionCode_v_8_median_y    0.000029     0\n",
       "324          model_v_0_max_y    0.000032     0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "select = feature_importance_df\n",
    "display(feature_importance_df[feature_importance_df['importance']>0].head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model_xgb(x_train,y_train):\n",
    "    model = xgb.XGBRegressor(n_estimators=150, learning_rate=0.1, gamma=0, subsample=0.8,\\\n",
    "        colsample_bytree=0.9, max_depth=7) #, objective ='reg:squarederror'\n",
    "    model.fit(x_train, y_train)\n",
    "    return model\n",
    "\n",
    "def build_model_lgb(x_train,y_train):\n",
    "    estimator = lgb.LGBMRegressor(num_leaves=127,n_estimators = 150)\n",
    "    param_grid = {\n",
    "        'learning_rate': [0.1],#\n",
    "    }\n",
    "    gbm = GridSearchCV(estimator, param_grid)\n",
    "\n",
    "    gbm.fit(x_train, y_train)\n",
    "    print(f\"Best:  {gbm}\" )\n",
    "\n",
    "    return gbm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Split data with val\n",
    "x_train,x_val,y_train,y_val = train_test_split(X_data,Y_data,test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sta of label:\n",
      "_min 0.0\n",
      "_max: 100000.0\n",
      "_mean 5599.181116\n",
      "_ptp 100000.0\n",
      "_std 7470.932963236522\n",
      "_var 55814839.341174036\n"
     ]
    }
   ],
   "source": [
    "## 定义了一个统计函数，方便后续信息统计\n",
    "def Sta_inf(data):\n",
    "    print('_min',np.min(data))\n",
    "    print('_max:',np.max(data))\n",
    "    print('_mean',np.mean(data))\n",
    "    print('_ptp',np.ptp(data))\n",
    "    print('_std',np.std(data))\n",
    "    print('_var',np.var(data))\n",
    "    \n",
    "print('Sta of label:')\n",
    "Sta_inf(Y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "553"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = list(set(X_data.columns))\n",
    "len(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train lgb...\n",
      "Predict lgb...\n"
     ]
    }
   ],
   "source": [
    "print('Predict lgb...')\n",
    "model_lgb_pre = build_model_lgb(X_data[col],Y_data)\n",
    "subA_lgb = model_lgb_pre.predict(X_test[col])\n",
    "print('Sta of Predict lgb:')\n",
    "Sta_inf(subA_lgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Train xgb...')\n",
    "model_xgb = build_model_xgb(x_train[col],y_train)\n",
    "val_xgb = model_xgb.predict(x_val[col])\n",
    "MAE_xgb = mean_absolute_error(y_val,val_xgb)\n",
    "print('MAE of val with xgb:',MAE_xgb)\n",
    "\n",
    "print('Predict xgb...')\n",
    "model_xgb_pre = build_model_xgb(X_data[col],Y_data)\n",
    "subA_xgb = model_xgb_pre.predict(X_test)\n",
    "print('Sta of Predict xgb:')\n",
    "Sta_inf(subA_xgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 这里我们采取了简单的加权融合的方式\n",
    "val_Weighted = (1-MAE_lgb/(MAE_xgb+MAE_lgb))*val_lgb+(1-MAE_xgb/(MAE_xgb+MAE_lgb))*val_xgb\n",
    "val_Weighted[val_Weighted<10]=10 # 由于我们发现预测的最小值有负数，而真实情况下，price为负是不存在的，由此我们进行对应的后修正\n",
    "print('MAE of val with Weighted ensemble:',mean_absolute_error(y_val,val_Weighted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_Weighted = (1-MAE_lgb/(MAE_xgb+MAE_lgb))*subA_lgb+(1-MAE_xgb/(MAE_xgb+MAE_lgb))*subA_xgb\n",
    "sub_Weighted[sub_Weighted<10]=10\n",
    "## 查看预测值的统计,判断和训练集是否接近\n",
    "print('Sta of Predict lgb:')\n",
    "Sta_inf(sub_Weighted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Sta_inf(Train_data.price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SaleID</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>250000</td>\n",
       "      <td>720326</td>\n",
       "      <td>5332.065801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>250001</td>\n",
       "      <td>714316</td>\n",
       "      <td>1253.872689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>250002</td>\n",
       "      <td>704693</td>\n",
       "      <td>2826.833345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>250003</td>\n",
       "      <td>624972</td>\n",
       "      <td>621.176465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>250004</td>\n",
       "      <td>669753</td>\n",
       "      <td>6743.951511</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        SaleID        price\n",
       "250000  720326  5332.065801\n",
       "250001  714316  1253.872689\n",
       "250002  704693  2826.833345\n",
       "250003  624972   621.176465\n",
       "250004  669753  6743.951511"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub3 = test[['SaleID']].copy()\n",
    "sub3['price'] = sub_Weighted\n",
    "sub3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f600672bc8>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sub3['price'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "now = time.strftime(\"%m-%d_%H-%M\", time.localtime())\n",
    "sub3.to_csv('./sub/sub_'+now+'.csv',index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
